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F5A6D2AE83A7989E17704E69F0A640368C676594 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/expressionbuilder.html?context=cdpaas&locale=en | Expression Builder (SPSS Modeler) | Expression Builder
You can type CLEM expressions manually or use the Expression Builder, which displays a complete list of CLEM functions and operators as well as data fields from the current flow, allowing you to quickly build expressions without memorizing the exact names of fields or functions.
The Expression Bu... | # Expression Builder #
You can type CLEM expressions manually or use the Expression Builder, which displays a complete list of CLEM functions and operators as well as data fields from the current flow, allowing you to quickly build expressions without memorizing the exact names of fields or functions\.
The Expression... | <!doctype html>
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<meta name="description" content="You can type CLEM expressions manually or use the Expression Builder, w... |
9DA0D100A88228AB463CB9B1B6CF1C051253911A | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/expressionbuilder_functions.html?context=cdpaas&locale=en | Selecting functions (SPSS Modeler) | Selecting functions
The function list displays all available CLEM functions and operators. Scroll to select a function from the list, or, for easier searching, use the drop-down list to display a subset of functions or operators.
The following categories of functions are available:
Table 1. CLEM functions for us... | # Selecting functions #
The function list displays all available CLEM functions and operators\. Scroll to select a function from the list, or, for easier searching, use the drop\-down list to display a subset of functions or operators\.
The following categories of functions are available:
<!-- <table "summary="" id=... | <!doctype html>
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1B0AB9084C7DD9546BDC2F376B58E32C0ECFEE85 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_build.html?context=cdpaas&locale=en | Extension Model node (SPSS Modeler) | Extension Model node
With the Extension Model node, you can run R scripts or Python for Spark scripts to build and score models.
After adding the node to your canvas, double-click the node to open its properties.
| # Extension Model node #
With the Extension Model node, you can run R scripts or Python for Spark scripts to build and score models\.
After adding the node to your canvas, double\-click the node to open its properties\.
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| <!doctype html>
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6402316FEBFAD11A582D9C567811003F4BEE596A | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_export.html?context=cdpaas&locale=en | Extension Export node (SPSS Modeler) | Extension Export node
You can use the Extension Export node to run R scripts or Python for Spark scripts to export data.
| # Extension Export node #
You can use the Extension Export node to run R scripts or Python for Spark scripts to export data\.
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378F6A8306234029DE1642CBFF8E44ED6848BF74 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_importer.html?context=cdpaas&locale=en | Extension Import node (SPSS Modeler) | Extension Import node
With the Extension Import node, you can run R scripts or Python for Spark scripts to import data.
After adding the node to your canvas, double-click the node to open its properties.
| # Extension Import node #
With the Extension Import node, you can run R scripts or Python for Spark scripts to import data\.
After adding the node to your canvas, double\-click the node to open its properties\.
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<html lang="en-us">
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97FA49D526786021CF325FF9AFF15646A8270B48 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_nativepython_api.html?context=cdpaas&locale=en | Native Python APIs (SPSS Modeler) | Native Python APIs
You can invoke native Python APIs from your scripts to interact with SPSS Modeler.
The following APIs are supported.
To see an example, you can download the sample stream [python-extension-str.zip](https://github.com/IBMDataScience/ModelerFlowsExamples/blob/main/samples) and import it into SPS... | # Native Python APIs #
You can invoke native Python APIs from your scripts to interact with SPSS Modeler\.
The following APIs are supported\.
To see an example, you can download the sample stream [python\-extension\-str\.zip](https://github.com/IBMDataScience/ModelerFlowsExamples/blob/main/samples) and import it in... | <!doctype html>
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<meta name="description" content="You can invoke native Python APIs from your scripts to interact with SP... |
1D46D1240377AEA562F14A560CB9F24DF33EDF88 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_output.html?context=cdpaas&locale=en | Extension Output node (SPSS Modeler) | Extension Output node
With the Extension Output node, you can run R scripts or Python for Spark scripts to produce output.
After adding the node to your canvas, double-click the node to open its properties.
| # Extension Output node #
With the Extension Output node, you can run R scripts or Python for Spark scripts to produce output\.
After adding the node to your canvas, double\-click the node to open its properties\.
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<meta name="description" content="With the Extension Output node, you can run R scripts or Python for Spa... |
FF6C435ADBD62DE03C06CE4F90343D3CD04F9E8F | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_process.html?context=cdpaas&locale=en | Extension Transform node (SPSS Modeler) | Extension Transform node
With the Extension Transform node, you can take data from an SPSS Modeler flow and apply transformations to the data using R scripting or Python for Spark scripting.
When the data has been modified, it's returned to the flow for further processing, model building, and model scoring. The Ex... | # Extension Transform node #
With the Extension Transform node, you can take data from an SPSS Modeler flow and apply transformations to the data using R scripting or Python for Spark scripting\.
When the data has been modified, it's returned to the flow for further processing, model building, and model scoring\. Th... | <!doctype html>
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<meta name="description" content="With the Extension Transform node, you can take data from an SPSS Model... |
63C0DFB695860E1DA7981D86959D998BEBC2DD03 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark.html?context=cdpaas&locale=en | Python for Spark scripts (SPSS Modeler) | Python for Spark scripts
SPSS Modeler supports Python scripts for Apache Spark.
Note:
* Python nodes depend on the Spark environment.
* Python scripts must use the Spark API because data is presented in the form of a Spark DataFrame.
* When installing Python, make sure all users have permission to access the ... | # Python for Spark scripts #
SPSS Modeler supports Python scripts for Apache Spark\.
Note:
<!-- <ul> -->
* Python nodes depend on the Spark environment\.
* Python scripts must use the Spark API because data is presented in the form of a Spark DataFrame\.
* When installing Python, make sure all users have perm... | <!doctype html>
<html lang="en-us">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="SPSS Modeler supports Python scripts for Apache Spark.">
<meta name="... |
17470065AFC59337B207721AB539B4622BBB3055 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark_api.html?context=cdpaas&locale=en | Scripting with Python for Spark (SPSS Modeler) | Scripting with Python for Spark
SPSS Modeler can run Python scripts using the Apache Spark framework to process data. This documentation provides the Python API description for the interfaces provided.
The SPSS Modeler installation includes a Spark distribution.
| # Scripting with Python for Spark #
SPSS Modeler can run Python scripts using the Apache Spark framework to process data\. This documentation provides the Python API description for the interfaces provided\.
The SPSS Modeler installation includes a Spark distribution\.
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| <!doctype html>
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7436F8933CA1DD44E05CD59F8E2CB13052763643 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark_date.html?context=cdpaas&locale=en | Date, time, timestamp (SPSS Modeler) | Date, time, timestamp
For operations that use date, time, or timestamp type data, the value is converted to the real value based on the value 1970-01-01:00:00:00 (using Coordinated Universal Time).
For the date, the value represents the number of days, based on the value 1970-01-01 (using Coordinated Universal Time... | # Date, time, timestamp #
For operations that use **date**, **time**, or **timestamp** type data, the value is converted to the real value based on the value `1970-01-01:00:00:00` (using Coordinated Universal Time)\.
For the **date**, the value represents the number of days, based on the value `1970-01-01` (using Coo... | <!doctype html>
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<meta name="geo.country" content="ZZ">
<script>
digitalData = {
page: {
pageInfo: {
... |
835B998310E6E268F648D4AA28528190EBBB48CA | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark_examples.html?context=cdpaas&locale=en | Examples (SPSS Modeler) | Examples
This section provides Python for Spark scripting examples.
| # Examples #
This section provides Python for Spark scripting examples\.
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| <!doctype html>
<html lang="en-us">
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<meta na... |
AD61BC1B395A071D8850BC2405A8C311CFDC931F | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark_exceptions.html?context=cdpaas&locale=en | Exceptions (SPSS Modeler) | Exceptions
This section describes possible exception instances. They are all a subclass of python exception.
| # Exceptions #
This section describes possible exception instances\. They are all a subclass of python exception\.
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<meta name="description" content="This section describes possible exception instances. They are all a sub... |
450CAAACD51ABDEDAB940CAFB4BC47EBFBCBBA67 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark_metadata.html?context=cdpaas&locale=en | Data metadata (SPSS Modeler) | Data metadata
This section describes how to set up the data model attributes based on pyspark.sql.StructField.
| # Data metadata #
This section describes how to set up the data model attributes based on `pyspark.sql.StructField`\.
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<meta name="description" content="This section describes how to set up the data model attributes based on... |
B98506EB96C587BDFD06CBF67617E25D9DAE8E60 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_r.html?context=cdpaas&locale=en | R scripts (SPSS Modeler) | R scripts
SPSS Modeler supports R scripts.
| # R scripts #
SPSS Modeler supports R scripts\.
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<meta name="geo.country" content="... |
50636405C61E0AF7D2EE0EE31256C4CD0F6C5DED | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/factor.html?context=cdpaas&locale=en | PCA/Factor node (SPSS Modeler) | PCA/Factor node
The PCA/Factor node provides powerful data-reduction techniques to reduce the complexity of your data. Two similar but distinct approaches are provided.
* Principal components analysis (PCA) finds linear combinations of the input fields that do the best job of capturing the variance in the entire... | # PCA/Factor node #
The PCA/Factor node provides powerful data\-reduction techniques to reduce the complexity of your data\. Two similar but distinct approaches are provided\.
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* Principal components analysis (PCA) finds linear combinations of the input fields that do the best job of capturing the var... | <!doctype html>
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<meta name="description" content="The PCA/Factor node provides powerful data-reduction techniques to redu... |
9E1CDB994E758D43D9D8CDC5D88E2B5C7E0088D7 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/featureselection.html?context=cdpaas&locale=en | Feature Selection node (SPSS Modeler) | Feature Selection node
Data mining problems may involve hundreds, or even thousands, of fields that can potentially be used as inputs. As a result, a great deal of time and effort may be spent examining which fields or variables to include in the model. To narrow down the choices, the Feature Selection algorithm can... | # Feature Selection node #
Data mining problems may involve hundreds, or even thousands, of fields that can potentially be used as inputs\. As a result, a great deal of time and effort may be spent examining which fields or variables to include in the model\. To narrow down the choices, the Feature Selection algorithm... | <!doctype html>
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="Data mining problems may involve hundreds, or even thousands, of fields... |
38D24508B131BEB6138652C2FD1E0380A001BB54 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/filler.html?context=cdpaas&locale=en | Filler node (SPSS Modeler) | Filler node
Filler nodes are used to replace field values and change storage. You can choose to replace values based on a specified CLEM condition, such as @BLANK(FIELD). Alternatively, you can choose to replace all blanks or null values with a specific value. Filler nodes are often used in conjunction with the Type... | # Filler node #
Filler nodes are used to replace field values and change storage\. You can choose to replace values based on a specified CLEM condition, such as `@BLANK(FIELD)`\. Alternatively, you can choose to replace all blanks or null values with a specific value\. Filler nodes are often used in conjunction with t... | <!doctype html>
<html lang="en-us">
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="Filler nodes are used to replace field values and change storage. You c... |
EED64F79EBFDD957DEEBEC6261B3A70A248F3D35 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/filter.html?context=cdpaas&locale=en | Filter node (SPSS Modeler) | Filter node
You can rename or exclude fields at any point in a flow. For example, as a medical researcher, you may not be concerned about the potassium level (field-level data) of patients (record-level data); therefore, you can filter out the K (potassium) field. This can be done using a separate Filter node or usi... | # Filter node #
You can rename or exclude fields at any point in a flow\. For example, as a medical researcher, you may not be concerned about the potassium level (field\-level data) of patients (record\-level data); therefore, you can filter out the `K` (potassium) field\. This can be done using a separate Filter nod... | <!doctype html>
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="You can rename or exclude fields at any point in a flow. For example, a... |
B8522E9801281DD4118A5012ACF885A7EC2354E4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/genlin.html?context=cdpaas&locale=en | GenLin node (SPSS Modeler) | GenLin node
The generalized linear model expands the general linear model so that the dependent variable is linearly related to the factors and covariates via a specified link function. Moreover, the model allows for the dependent variable to have a non-normal distribution. It covers widely used statistical models, ... | # GenLin node #
The generalized linear model expands the general linear model so that the dependent variable is linearly related to the factors and covariates via a specified link function\. Moreover, the model allows for the dependent variable to have a non\-normal distribution\. It covers widely used statistical mod... | <!doctype html>
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<meta name="description" content="The generalized linear model expands the general linear model so that t... |
CF6FE4E4058C24F0BEB94D379FB9E820C09456D2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/gle.html?context=cdpaas&locale=en | GLE node (SPSS Modeler) | GLE node
The GLE model identifies the dependent variable that is linearly related to the factors and covariates via a specified link function. Moreover, the model allows for the dependent variable to have a non-normal distribution. It covers widely used statistical models, such as linear regression for normally dist... | # GLE node #
The GLE model identifies the dependent variable that is linearly related to the factors and covariates via a specified link function\. Moreover, the model allows for the dependent variable to have a non\-normal distribution\. It covers widely used statistical models, such as linear regression for normally... | <!doctype html>
<html lang="en-us">
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="UTF-8">
<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="The GLE model identifies the dependent variable that is linearly relate... |
B561F461842BB0D185F097E0ADB8D3AC13266172 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/glmm.html?context=cdpaas&locale=en | GLMM node (SPSS Modeler) | GLMM node
This node creates a generalized linear mixed model (GLMM).
Generalized linear mixed models extend the linear model so that:
* The target is linearly related to the factors and covariates via a specified link function
* The target can have a non-normal distribution
* The observations can be correlate... | # GLMM node #
This node creates a generalized linear mixed model (GLMM)\.
Generalized linear mixed models extend the linear model so that:
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* The target is linearly related to the factors and covariates via a specified link function
* The target can have a non\-normal distribution
* The observati... | <!doctype html>
<html lang="en-us">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="UTF-8">
<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="This node creates a generalized linear mixed model (GLMM).">
<meta na... |
E6B5EAD096E68A255C5526ADD4C828534891C090 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/gmm.html?context=cdpaas&locale=en | Gaussian Mixture node (SPSS Modeler) | Gaussian Mixture node
A Gaussian Mixture© model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.
One can think of mixture models as generalizing k-means clustering to incorporate information about the covaria... | # Gaussian Mixture node #
A Gaussian Mixture© model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters\.
One can think of mixture models as generalizing k\-means clustering to incorporate information about the cov... | <!doctype html>
<html lang="en-us">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="A Gaussian Mixture© model is a probabilistic model that assumes all the... |
A1FE4B06DB60F8A9C916FBEAF5C7482155BD62E3 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/hdbscan.html?context=cdpaas&locale=en | HDBSCAN node (SPSS Modeler) | HDBSCAN node
Hierarchical Density-Based Spatial Clustering (HDBSCAN)© uses unsupervised learning to find clusters, or dense regions, of a data set.
The HDBSCAN node in watsonx.ai exposes the core features and commonly used parameters of the HDBSCAN library. The node is implemented in Python, and you can use it to ... | # HDBSCAN node #
Hierarchical Density\-Based Spatial Clustering (HDBSCAN)© uses unsupervised learning to find clusters, or dense regions, of a data set\.
The HDBSCAN node in watsonx\.ai exposes the core features and commonly used parameters of the HDBSCAN library\. The node is implemented in Python, and you can use ... | <!doctype html>
<html lang="en-us">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="UTF-8">
<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="Hierarchical Density-Based Spatial Clustering (HDBSCAN)© uses unsupervi... |
13F7C9C7B52EC7152F2B3D81B6EB42DB0319A6F4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/histogram.html?context=cdpaas&locale=en | Histogram node (SPSS Modeler) | Histogram node
Histogram nodes show the occurrence of values for numeric fields. They are often used to explore the data before manipulations and model building. Similar to the Distribution node, Histogram nodes are frequently used to reveal imbalances in the data.
Note: To show the occurrence of values for symboli... | # Histogram node #
Histogram nodes show the occurrence of values for numeric fields\. They are often used to explore the data before manipulations and model building\. Similar to the Distribution node, Histogram nodes are frequently used to reveal imbalances in the data\.
Note: To show the occurrence of values for sy... | <!doctype html>
<html lang="en-us">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="UTF-8">
<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="Histogram nodes show the occurrence of values for numeric fields. They ... |
00205C92C52FA28DB619EE1F9C8D76FE8564DB88 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/history.html?context=cdpaas&locale=en | History node (SPSS Modeler) | History node
History nodes are most often used for sequential data, such as time series data.
They are used to create new fields containing data from fields in previous records. When using a History node, you may want to use data that is presorted by a particular field. You can use a Sort node to do this.
| # History node #
History nodes are most often used for sequential data, such as time series data\.
They are used to create new fields containing data from fields in previous records\. When using a History node, you may want to use data that is presorted by a particular field\. You can use a Sort node to do this\.
<!... | <!doctype html>
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="UTF-8">
<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="description" content="History nodes are most often used for sequential data, such as time ser... |
1BC1FE73146C70FA2A76241470314A4732EFD918 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/isotonicas.html?context=cdpaas&locale=en | Isotonic-AS node (SPSS Modeler) | Isotonic-AS node
Isotonic Regression belongs to the family of regression algorithms. The Isotonic-AS node in watsonx.ai is implemented in Spark.
For details, see [Isotonic regression](https://spark.apache.org/docs/2.2.0/mllib-isotonic-regression.html). ^1^
^1^ "Regression - RDD-based API." Apache Spark. MLlib: Ma... | # Isotonic\-AS node #
Isotonic Regression belongs to the family of regression algorithms\. The Isotonic\-AS node in watsonx\.ai is implemented in Spark\.
For details, see [Isotonic regression](https://spark.apache.org/docs/2.2.0/mllib-isotonic-regression.html)\. ^1^
^1^ "Regression \- RDD\-based API\." *Apache Spar... | <!doctype html>
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22A8F7539D1374784E9BF247B1370C430910F43D | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/kdemodel.html?context=cdpaas&locale=en | KDE node (SPSS Modeler) | KDE node
Kernel Density Estimation (KDE)© uses the Ball Tree or KD Tree algorithms for efficient queries, and walks the line between unsupervised learning, feature engineering, and data modeling.
Neighbor-based approaches such as KDE are some of the most popular and useful density estimation techniques. KDE can be ... | # KDE node #
Kernel Density Estimation (KDE)© uses the Ball Tree or KD Tree algorithms for efficient queries, and walks the line between unsupervised learning, feature engineering, and data modeling\.
Neighbor\-based approaches such as KDE are some of the most popular and useful density estimation techniques\. KDE ca... | <!doctype html>
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033E2B1CD9E006383C2D2C045B8834BFBBAB0F09 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/kdesimulation.html?context=cdpaas&locale=en | KDE Simulation node (SPSS Modeler) | KDE Simulation node
Kernel Density Estimation (KDE)© uses the Ball Tree or KD Tree algorithms for efficient queries, and walks the line between unsupervised learning, feature engineering, and data modeling.
Neighbor-based approaches such as KDE are some of the most popular and useful density estimation techniques. ... | # KDE Simulation node #
Kernel Density Estimation (KDE)© uses the Ball Tree or KD Tree algorithms for efficient queries, and walks the line between unsupervised learning, feature engineering, and data modeling\.
Neighbor\-based approaches such as KDE are some of the most popular and useful density estimation techniqu... | <!doctype html>
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13A1FF3338F4AC1EB2CF3FF6781283B49AC8B5A6 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/kmeans.html?context=cdpaas&locale=en | K-Means node (SPSS Modeler) | K-Means node
The K-Means node provides a method of cluster analysis. It can be used to cluster the dataset into distinct groups when you don't know what those groups are at the beginning. Unlike most learning methods in SPSS Modeler, K-Means models do not use a target field. This type of learning, with no target fi... | # K\-Means node #
The K\-Means node provides a method of cluster analysis\. It can be used to cluster the dataset into distinct groups when you don't know what those groups are at the beginning\. Unlike most learning methods in SPSS Modeler, K\-Means models do not use a target field\. This type of learning, with no t... | <!doctype html>
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DCE39CA6C888CA6D5CF3F9B9D18D06FD3BD2DFBE | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/kmeansas.html?context=cdpaas&locale=en | K-Means-AS node (SPSS Modeler) | K-Means-AS node
K-Means is one of the most commonly used clustering algorithms. It clusters data points into a predefined number of clusters. The K-Means-AS node in SPSS Modeler is implemented in Spark.
See [K-Means Algorithms](https://spark.apache.org/docs/2.2.0/ml-clustering.html) for more details.^1^
Note that... | # K\-Means\-AS node #
K\-Means is one of the most commonly used clustering algorithms\. It clusters data points into a predefined number of clusters\. The K\-Means\-AS node in SPSS Modeler is implemented in Spark\.
See [K\-Means Algorithms](https://spark.apache.org/docs/2.2.0/ml-clustering.html) for more details\.^1... | <!doctype html>
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1DD1ED59E93DA4F6576E7EB1E420213AB34DD1DD | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/knn.html?context=cdpaas&locale=en | KNN node (SPSS Modeler) | KNN node
Nearest Neighbor Analysis is a method for classifying cases based on their similarity to other cases. In machine learning, it was developed as a way to recognize patterns of data without requiring an exact match to any stored patterns, or cases. Similar cases are near each other and dissimilar cases are dis... | # KNN node #
Nearest Neighbor Analysis is a method for classifying cases based on their similarity to other cases\. In machine learning, it was developed as a way to recognize patterns of data without requiring an exact match to any stored patterns, or cases\. Similar cases are near each other and dissimilar cases are... | <!doctype html>
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F965BE0F67B8B3C26BE38939A33FA8AB74AEA4CC | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/kohonen.html?context=cdpaas&locale=en | Kohonen node (SPSS Modeler) | Kohonen node
Kohonen networks are a type of neural network that perform clustering, also known as a knet or a self-organizing map. This type of network can be used to cluster the dataset into distinct groups when you don't know what those groups are at the beginning. Records are grouped so that records within a grou... | # Kohonen node #
Kohonen networks are a type of neural network that perform clustering, also known as a knet or a self\-organizing map\. This type of network can be used to cluster the dataset into distinct groups when you don't know what those groups are at the beginning\. Records are grouped so that records within a... | <!doctype html>
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67241853FC2471C6C0719F1B98E40625358B2E19 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/languageidentifier.html?context=cdpaas&locale=en | Reading in source text (SPSS Modeler) | Reading in source text
You can use the Language Identifier node to identify the natural language of a text field within your source data. The output of this node is a derived field that contains the detected language code.
![Language Identifier node](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/ta_lan... | # Reading in source text #
You can use the Language Identifier node to identify the natural language of a text field within your source data\. The output of this node is a derived field that contains the detected language code\.
![Language Identifier node](https://dataplatform.cloud.ibm.com/docs/content/wsd/images/ta... | <!doctype html>
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FC8006009802AE14770BE53062787D8A392B0070 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/linear.html?context=cdpaas&locale=en | Linear node (SPSS Modeler) | Linear node
Linear regression is a common statistical technique for classifying records based on the values of numeric input fields. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values.
Requirements. Only numeric fields can be used in a linea... | # Linear node #
Linear regression is a common statistical technique for classifying records based on the values of numeric input fields\. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values\.
Requirements\. Only numeric fields can be used in a ... | <!doctype html>
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2D9ACE87F4859BF7EF8CDF4EBBF8307C51034471 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/linearas.html?context=cdpaas&locale=en | Linear-AS node (SPSS Modeler) | Linear-AS node
Linear regression is a common statistical technique for classifying records based on the values of numeric input fields. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values.
Requirements. Only numeric fields and categorical pre... | # Linear\-AS node #
Linear regression is a common statistical technique for classifying records based on the values of numeric input fields\. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values\.
Requirements\. Only numeric fields and categoric... | <!doctype html>
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DE0C1913D6D770641762ED518FEFE8FFFC5A1F13 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/logreg.html?context=cdpaas&locale=en | Logistic node (SPSS Modeler) | Logistic node
Logistic regression, also known as nominal regression, is a statistical technique for classifying records based on values of input fields. It is analogous to linear regression but takes a categorical target field instead of a numeric one. Both binomial models (for targets with two discrete categories) ... | # Logistic node #
Logistic regression, also known as nominal regression, is a statistical technique for classifying records based on values of input fields\. It is analogous to linear regression but takes a categorical target field instead of a numeric one\. Both binomial models (for targets with two discrete categori... | <!doctype html>
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A9E9D62E92156CEBC0D4619CDE322AF48CACE913 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/lsvm.html?context=cdpaas&locale=en | LSVM node (SPSS Modeler) | LSVM node
With the LSVM node, you can use a linear support vector machine to classify data. LSVM is particularly suited for use with wide datasets--that is, those with a large number of predictor fields. You can use the default settings on the node to produce a basic model relatively quickly, or you can use the buil... | # LSVM node #
With the LSVM node, you can use a linear support vector machine to classify data\. LSVM is particularly suited for use with wide datasets\-\-that is, those with a large number of predictor fields\. You can use the default settings on the node to produce a basic model relatively quickly, or you can use th... | <!doctype html>
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774FD49C617DAC62F48EB31E08757E0AEC3D1282 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/matrix.html?context=cdpaas&locale=en | Matrix node (SPSS Modeler) | Matrix node
Use the Matrix to create a table that shows relationships between fields. It is most commonly used to show the relationship between two categorical fields (flag, nominal, or ordinal), but it can also be used to show relationships between continuous (numeric range) fields.
| # Matrix node #
Use the Matrix to create a table that shows relationships between fields\. It is most commonly used to show the relationship between two categorical fields (flag, nominal, or ordinal), but it can also be used to show relationships between continuous (numeric range) fields\.
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7B586E10794F26EA2654A7F7C34EC9EA48C8BFD4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/means.html?context=cdpaas&locale=en | Means node (SPSS Modeler) | Means node
The Means node compares the means between independent groups or between pairs of related fields to test whether a significant difference exists. For example, you can compare mean revenues before and after running a promotion or compare revenues from customers who didn't receive the promotion with those wh... | # Means node #
The Means node compares the means between independent groups or between pairs of related fields to test whether a significant difference exists\. For example, you can compare mean revenues before and after running a promotion or compare revenues from customers who didn't receive the promotion with those... | <!doctype html>
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6647035446FC3A28586EBABC619D10DB5FE3F4FD | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/merge.html?context=cdpaas&locale=en | Merge node (SPSS Modeler) | Merge node
The function of a Merge node is to take multiple input records and create a single output record containing all or some of the input fields. This is a useful operation when you want to merge data from different sources, such as internal customer data and purchased demographic data.
You can merge data in ... | # Merge node #
The function of a Merge node is to take multiple input records and create a single output record containing all or some of the input fields\. This is a useful operation when you want to merge data from different sources, such as internal customer data and purchased demographic data\.
You can merge data... | <!doctype html>
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61E8DF28E1A79B4BBA03CDA39F350BE5E55DAC7B | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/missingvalues_changes.html?context=cdpaas&locale=en | Functions available for missing values (SPSS Modeler) | Functions available for missing values
Different methods are available for dealing with missing values in your data. You may choose to use functionality available in Data Refinery or in nodes.
| # Functions available for missing values #
Different methods are available for dealing with missing values in your data\. You may choose to use functionality available in Data Refinery or in nodes\.
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0E5C87704E816097FF9E649620A1818798B5DB3F | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/missingvalues_fields.html?context=cdpaas&locale=en | Handling fields with missing values (SPSS Modeler) | Handling fields with missing values
If the majority of missing values are concentrated in a small number of fields, you can address them at the field level rather than at the record level. This approach also allows you to experiment with the relative importance of particular fields before deciding on an approach for... | # Handling fields with missing values #
If the majority of missing values are concentrated in a small number of fields, you can address them at the field level rather than at the record level\. This approach also allows you to experiment with the relative importance of particular fields before deciding on an approach ... | <!doctype html>
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D5FAFC625D1A1D0793D9521351E9B59A04AF00E9 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/missingvalues_overview.html?context=cdpaas&locale=en | Missing data values (SPSS Modeler) | Missing data values
During the data preparation phase of data mining, you will often want to replace missing values in the data.
Missing values are values in the data set that are unknown, uncollected, or incorrectly entered. Usually, such values aren't valid for their fields. For example, the field Sex should cont... | # Missing data values #
During the data preparation phase of data mining, you will often want to replace missing values in the data\.
Missing values are values in the data set that are unknown, uncollected, or incorrectly entered\. Usually, such values aren't valid for their fields\. For example, the field `Sex` shou... | <!doctype html>
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FE9FF9F5CC449798C00D008182F55BDAA91E546C | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/missingvalues_records.html?context=cdpaas&locale=en | Handling records with missing values (SPSS Modeler) | Handling records with missing values
If the majority of missing values are concentrated in a small number of records, you can just exclude those records. For example, a bank usually keeps detailed and complete records on its loan customers.
If, however, the bank is less restrictive in approving loans for its own st... | # Handling records with missing values #
If the majority of missing values are concentrated in a small number of records, you can just exclude those records\. For example, a bank usually keeps detailed and complete records on its loan customers\.
If, however, the bank is less restrictive in approving loans for its ow... | <!doctype html>
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3BA46A09CF64CE6120BE65C44614995B50B67DA1 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/missingvalues_system.html?context=cdpaas&locale=en | Handling records with system missing values (SPSS Modeler) | Handling records with system missing values
| # Handling records with system missing values #
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01C8222216B795904018497993CC5E44D51A3B35 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/missingvalues_treating.html?context=cdpaas&locale=en | Handling missing values (SPSS Modeler) | Handling missing values
You should decide how to treat missing values in light of your business or domain knowledge. To ease training time and increase accuracy, you may want to remove blanks from your data set. On the other hand, the presence of blank values may lead to new business opportunities or additional insi... | # Handling missing values #
You should decide how to treat missing values in light of your business or domain knowledge\. To ease training time and increase accuracy, you may want to remove blanks from your data set\. On the other hand, the presence of blank values may lead to new business opportunities or additional ... | <!doctype html>
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6576530EC5D705B8BF323F6C459C32A87AE3F9A4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/mlpas.html?context=cdpaas&locale=en | MultiLayerPerceptron-AS node (SPSS Modeler) | MultiLayerPerceptron-AS node
Multilayer perceptron is a classifier based on the feedforward artificial neural network and consists of multiple layers.
Each layer is fully connected to the next layer in the network. See [Multilayer Perceptron Classifier (MLPC)](https://spark.apache.org/docs/latest/ml-classification-... | # MultiLayerPerceptron\-AS node #
Multilayer perceptron is a classifier based on the feedforward artificial neural network and consists of multiple layers\.
Each layer is fully connected to the next layer in the network\. See [Multilayer Perceptron Classifier (MLPC)](https://spark.apache.org/docs/latest/ml-classifica... | <!doctype html>
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5F0FC43F57AB9AF130DEA6A795E1E81A6AA95ACC | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/multiplot.html?context=cdpaas&locale=en | Multiplot node (SPSS Modeler) | Multiplot node
A multiplot is a special type of plot that displays multiple Y fields over a single X field. The Y fields are plotted as colored lines and each is equivalent to a Plot node with Style set to Line and X Mode set to Sort. Multiplots are useful when you have time sequence data and want to explore the f... | # Multiplot node #
A multiplot is a special type of plot that displays multiple `Y` fields over a single `X` field\. The `Y` fields are plotted as colored lines and each is equivalent to a Plot node with Style set to Line and X Mode set to Sort\. Multiplots are useful when you have time sequence data and want to exp... | <!doctype html>
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9F06DF311976F336CB3164B08D5DA7D6F93419E2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/neuralnetwork.html?context=cdpaas&locale=en | Neural Net node (SPSS Modeler) | Neural Net node
A neural network can approximate a wide range of predictive models with minimal demands on model structure and assumption. The form of the relationships is determined during the learning process. If a linear relationship between the target and predictors is appropriate, the results of the neural netw... | # Neural Net node #
A neural network can approximate a wide range of predictive models with minimal demands on model structure and assumption\. The form of the relationships is determined during the learning process\. If a linear relationship between the target and predictors is appropriate, the results of the neural ... | <!doctype html>
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9933646421686556C9AE8459EE2E51ED9DAB1C33 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/nodes_cache_disable.html?context=cdpaas&locale=en | Disabling or caching nodes in a flow (SPSS Modeler) | Disabling or caching nodes in a flow
You can disable a node so it's ignored when the flow runs. And you can set up a cache on a node.
| # Disabling or caching nodes in a flow #
You can disable a node so it's ignored when the flow runs\. And you can set up a cache on a node\.
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759B6927189FEA6BE3124BF79FA527873CB84EA6 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/ocsvm.html?context=cdpaas&locale=en | One-Class SVM node (SPSS Modeler) | One-Class SVM node
The One-Class SVM© node uses an unsupervised learning algorithm. The node can be used for novelty detection. It will detect the soft boundary of a given set of samples, to then classify new points as belonging to that set or not. This One-Class SVM modeling node is implemented in Python and requir... | # One\-Class SVM node #
The One\-Class SVM© node uses an unsupervised learning algorithm\. The node can be used for novelty detection\. It will detect the soft boundary of a given set of samples, to then classify new points as belonging to that set or not\. This One\-Class SVM modeling node is implemented in Python an... | <!doctype html>
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<meta name="description" content="The One-Class SVM© node uses an unsupervised learning algorithm. The no... |
98FC8E9A3380E4593D9BF08B78CE6A7797C0204B | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/partition.html?context=cdpaas&locale=en | Partition node (SPSS Modeler) | Partition node
Partition nodes are used to generate a partition field that splits the data into separate subsets or samples for the training, testing, and validation stages of model building. By using one sample to generate the model and a separate sample to test it, you can get a good indication of how well the mod... | # Partition node #
Partition nodes are used to generate a partition field that splits the data into separate subsets or samples for the training, testing, and validation stages of model building\. By using one sample to generate the model and a separate sample to test it, you can get a good indication of how well the ... | <!doctype html>
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CFC54BB4CEA29104BD4F9793B51ABE558AA0250D | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/plot.html?context=cdpaas&locale=en | Plot node (SPSS Modeler) | Plot node
Plot nodes show the relationship between numeric fields. You can create a plot using points (also known as a scatterplot), or you can use lines. You can create three types of line plots by specifying an X Mode in the node properties.
| # Plot node #
Plot nodes show the relationship between numeric fields\. You can create a plot using points (also known as a scatterplot), or you can use lines\. You can create three types of line plots by specifying an X Mode in the node properties\.
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5E2A4B92C4F5F84B3DDE2EAD6827C7FA89EB0565 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/quest.html?context=cdpaas&locale=en | QUEST node (SPSS Modeler) | QUEST node
QUEST—or Quick, Unbiased, Efficient Statistical Tree—is a binary classification method for building decision trees. A major motivation in its development was to reduce the processing time required for large C&R Tree analyses with either many variables or many cases. A second goal of QUEST was to reduce th... | # QUEST node #
QUEST—or Quick, Unbiased, Efficient Statistical Tree—is a binary classification method for building decision trees\. A major motivation in its development was to reduce the processing time required for large C&R Tree analyses with either many variables or many cases\. A second goal of QUEST was to reduc... | <!doctype html>
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2581DD8F04F917BA91F1201137AE0EFEA1F82E26 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/randomforest.html?context=cdpaas&locale=en | Random Forest node (SPSS Modeler) | Random Forest node
Random Forest© is an advanced implementation of a bagging algorithm with a tree model as the base model.
In random forests, each tree in the ensemble is built from a sample drawn with replacement (for example, a bootstrap sample) from the training set. When splitting a node during the constructio... | # Random Forest node #
Random Forest© is an advanced implementation of a bagging algorithm with a tree model as the base model\.
In random forests, each tree in the ensemble is built from a sample drawn with replacement (for example, a bootstrap sample) from the training set\. When splitting a node during the constru... | <!doctype html>
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01800E00BDFB7CFE0E751FA6C616160C48E6ED21 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/randomtrees.html?context=cdpaas&locale=en | Random Trees node (SPSS Modeler) | Random Trees node
The Random Trees node can be used with data in a distributed environment. In this node, you build an ensemble model that consists of multiple decision trees.
The Random Trees node is a tree-based classification and prediction method that is built on Classification and Regression Tree methodology. ... | # Random Trees node #
The Random Trees node can be used with data in a distributed environment\. In this node, you build an ensemble model that consists of multiple decision trees\.
The Random Trees node is a tree\-based classification and prediction method that is built on Classification and Regression Tree methodol... | <!doctype html>
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2D3F7F5EFB161E0D88AE69C4710D70AA99DB0BDE | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/reclassify.html?context=cdpaas&locale=en | Reclassify node (SPSS Modeler) | Reclassify node
The Reclassify node enables the transformation from one set of categorical values to another. Reclassification is useful for collapsing categories or regrouping data for analysis.
For example, you could reclassify the values for Product into three groups, such as Kitchenware, Bath and Linens, and Ap... | # Reclassify node #
The Reclassify node enables the transformation from one set of categorical values to another\. Reclassification is useful for collapsing categories or regrouping data for analysis\.
For example, you could reclassify the values for `Product` into three groups, such as `Kitchenware`, `Bath and Linen... | <!doctype html>
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BBDEDA771A051A9B1871F9BEC9589D91421E7C0C | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/regression.html?context=cdpaas&locale=en | Refression (SPSS Modeler) | Regression node
Linear regression is a common statistical technique for classifying records based on the values of numeric input fields. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values.
Requirements. Only numeric fields can be used in a r... | # Regression node #
Linear regression is a common statistical technique for classifying records based on the values of numeric input fields\. Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values\.
Requirements\. Only numeric fields can be used i... | <!doctype html>
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8322C981206A5C7EEEC48C32C9DDCEC9FCE98AEE | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/reorder.html?context=cdpaas&locale=en | Field Reorder node (SPSS Modeler) | Field Reorder node
With the Field Reorder node, you can define the natural order used to display fields downstream. This order affects the display of fields in a variety of places, such as tables, lists, and the Field Chooser.
This operation is useful, for example, when working with wide datasets to make fields of ... | # Field Reorder node #
With the Field Reorder node, you can define the natural order used to display fields downstream\. This order affects the display of fields in a variety of places, such as tables, lists, and the Field Chooser\.
This operation is useful, for example, when working with wide datasets to make fields... | <!doctype html>
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BF6A65F061558B6AED8A438A887B6474A0FDFFC3 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/report.html?context=cdpaas&locale=en | Report node (SPSS Modeler) | Report node
You can use the Report node to create formatted reports containing fixed text, data, or other expressions derived from the data. Specify the format of the report by using text templates to define the fixed text and the data output constructions. You can provide custom text formatting using HTML tags in t... | # Report node #
You can use the Report node to create formatted reports containing fixed text, data, or other expressions derived from the data\. Specify the format of the report by using text templates to define the fixed text and the data output constructions\. You can provide custom text formatting using HTML tags ... | <!doctype html>
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<meta name="description" content="You can use the Report node to create formatted reports containing fixe... |
36C8AF3BBAFFF1C227CF611D7327AFA8E378D6EC | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/restructure.html?context=cdpaas&locale=en | Restructure node (SPSS Modeler) | Restructure node
With the Restructure node, you can generate multiple fields based on the values of a nominal or flag field. The newly generated fields can contain values from another field or numeric flags (0 and 1). The functionality of this node is similar to that of the Set to Flag node. However, it offers more ... | # Restructure node #
With the Restructure node, you can generate multiple fields based on the values of a nominal or flag field\. The newly generated fields can contain values from another field or numeric flags (0 and 1)\. The functionality of this node is similar to that of the Set to Flag node\. However, it offers ... | <!doctype html>
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265714702B012F1010CE06D97EC16623360F4E2B | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/rfm_aggregate.html?context=cdpaas&locale=en | RFM Aggregate node (SPSS Modeler) | RFM Aggregate node
The Recency, Frequency, Monetary (RFM) Aggregate node allows you to take customers' historical transactional data, strip away any unused data, and combine all of their remaining transaction data into a single row (using their unique customer ID as a key) that lists when they last dealt with you (r... | # RFM Aggregate node #
The Recency, Frequency, Monetary (RFM) Aggregate node allows you to take customers' historical transactional data, strip away any unused data, and combine all of their remaining transaction data into a single row (using their unique customer ID as a key) that lists when they last dealt with you ... | <!doctype html>
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<meta name="description" content="The Recency, Frequency, Monetary (RFM) Aggregate node allows you to tak... |
9E15D946EDFB82EF911D36032C073CF1736B39DA | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/rfm_analysis.html?context=cdpaas&locale=en | RFM Analysis node (SPSS Modeler) | RFM Analysis node
You can use the Recency, Frequency, Monetary (RFM) Analysis node to determine quantitatively which customers are likely to be the best ones by examining how recently they last purchased from you (recency), how often they purchased (frequency), and how much they spent over all transactions (monetary... | # RFM Analysis node #
You can use the Recency, Frequency, Monetary (RFM) Analysis node to determine quantitatively which customers are likely to be the best ones by examining how recently they last purchased from you (recency), how often they purchased (frequency), and how much they spent over all transactions (moneta... | <!doctype html>
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AF3DA662099BD616B642F69925AEC7C8AFC84611 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/sample.html?context=cdpaas&locale=en | Sample node (SPSS Modeler) | Sample node
You can use Sample nodes to select a subset of records for analysis, or to specify a proportion of records to discard. A variety of sample types are supported, including stratified, clustered, and nonrandom (structured) samples.
Sampling can be used for several reasons:
* To improve performance by e... | # Sample node #
You can use Sample nodes to select a subset of records for analysis, or to specify a proportion of records to discard\. A variety of sample types are supported, including stratified, clustered, and nonrandom (structured) samples\.
Sampling can be used for several reasons:
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* To improv... | <!doctype html>
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84E8928D464D412B225638BCC41F2837F98AEF43 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/adpnodeslots.html?context=cdpaas&locale=en | autodataprepnode properties | autodataprepnode properties
The Auto Data Prep (ADP) node can analyze your data and identify fixes, screen out fields that are problematic or not likely to be useful, derive new a... | # autodataprepnode properties #
The Auto Data Prep (ADP) node can analyze your data and identify fixes, screen out fields that are problematic or not likely to be useful, derive new... | <!doctype html>
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8CD81C0F5F84DFE58834AEB8B71E6D7780B8DEAD | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/aggregatenodeslots.html?context=cdpaas&locale=en | aggregatenode properties | aggregatenode properties
 The Aggregate node replaces a sequence of input records with summarized, aggregated output records.
aggregatenode properties
Table 1. aggregatenode pr... | # aggregatenode properties #
 The Aggregate node replaces a sequence of input records with summarized, aggregated output records\.
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2C17E0A9E72FE65317838E81ACF1FA77620E0C6C | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/analysisnodeslots.html?context=cdpaas&locale=en | analysisnode properties | analysisnode properties
The Analysis node evaluates predictive models' ability to generate accurate predictions. Analysis nodes perform various comparisons between predicted values a... | # analysisnode properties #
The Analysis node evaluates predictive models' ability to generate accurate predictions\. Analysis nodes perform various comparisons between predicted value... | <!doctype html>
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5C2296329A2D24B1A22A3848731708D78949E74C | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/anomalydetectionnodeslots.html?context=cdpaas&locale=en | anomalydetectionnode properties | anomalydetectionnode properties
The Anomaly node identifies unusual cases, or outliers, that don't conform to patterns of "normal" data. With this node, it's possible to ident... | # anomalydetectionnode properties #
The Anomaly node identifies unusual cases, or outliers, that don't conform to patterns of "normal" data\. With this node, it's possible to id... | <!doctype html>
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B51FF1FBA515035A93290F353D20AD9D54BC043C | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/anomalydetectionnuggetnodeslots.html?context=cdpaas&locale=en | applyanomalydetectionnode properties | applyanomalydetectionnode properties
You can use Anomaly Detection modeling nodes to generate an Anomaly Detection model nugget. The scripting name of this model nugget is applyanomalydetectionnode. For more information on scripting the modeling node itself, see [anomalydetectionnode properties](https://dataplatform... | # applyanomalydetectionnode properties #
You can use Anomaly Detection modeling nodes to generate an Anomaly Detection model nugget\. The scripting name of this model nugget is *applyanomalydetectionnode*\. For more information on scripting the modeling node itself, see [anomalydetectionnode properties](https://datapl... | <!doctype html>
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65FFB2E27EACD57BCADC6C1646EB280212D3B2C2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/anonymizenodeslots.html?context=cdpaas&locale=en | anonymizenode properties | anonymizenode properties
The Anonymize node transforms the way field names and values are represented downstream, thus disguising the original data. This can be useful if you want ... | # anonymizenode properties #
The Anonymize node transforms the way field names and values are represented downstream, thus disguising the original data\. This can be useful if you wa... | <!doctype html>
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<meta name="geo.c... |
8D328FC36822024D739F83A36FEF66E5ABE61128 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/appendnodeslots.html?context=cdpaas&locale=en | appendnode properties | appendnode properties
 The Append node concatenates sets of records. It's useful for combining datasets with similar structures but different data.
appendnode properties
Table 1. app... | # appendnode properties #
 The Append node concatenates sets of records\. It's useful for combining datasets with similar structures but different data\.
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76EC742BC2D093C10C6A5B85456BFBB6571C416D | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/apriorinodeslots.html?context=cdpaas&locale=en | apriorinode properties | apriorinode properties
The Apriori node extracts a set of rules from the data, pulling out the rules with the highest information content. Apriori offers five different methods of sele... | # apriorinode properties #
The Apriori node extracts a set of rules from the data, pulling out the rules with the highest information content\. Apriori offers five different methods of s... | <!doctype html>
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<met... |
292C0E87B8E56B15991C954508AB125A8FB80972 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/apriorinuggetnodeslots.html?context=cdpaas&locale=en | applyapriorinode properties | applyapriorinode properties
You can use Apriori modeling nodes to generate an Apriori model nugget. The scripting name of this model nugget is applyapriorinode. For more information on scripting the modeling node itself, see [apriorinode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting... | # applyapriorinode properties #
You can use Apriori modeling nodes to generate an Apriori model nugget\. The scripting name of this model nugget is *applyapriorinode*\. For more information on scripting the modeling node itself, see [apriorinode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scr... | <!doctype html>
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... |
2BCBD3D61CC24296EA38B26B10306B7F50CE4988 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/astimeintervalnodeslots.html?context=cdpaas&locale=en | astimeintervalsnode properties | astimeintervalsnode properties
Use the Time Intervals node to specify intervals and derive a new time field for estimating or forecasting. A full range of time intervals is... | # astimeintervalsnode properties #
Use the Time Intervals node to specify intervals and derive a new time field for estimating or forecasting\. A full range of time intervals... | <!doctype html>
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<me... |
27963DF2327FBE202B836AC5905258D063A8770D | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/autoclassifiernuggetnodeslots.html?context=cdpaas&locale=en | applyautoclassifiernode properties | applyautoclassifiernode properties
You can use Auto Classifier modeling nodes to generate an Auto Classifier model nugget. The scripting name of this model nugget is applyautoclassifiernode. For more information on scripting the modeling node itself, see [autoclassifiernode properties](https://dataplatform.cloud.ibm... | # applyautoclassifiernode properties #
You can use Auto Classifier modeling nodes to generate an Auto Classifier model nugget\. The scripting name of this model nugget is *applyautoclassifiernode*\. For more information on scripting the modeling node itself, see [autoclassifiernode properties](https://dataplatform.clo... | <!doctype html>
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<meta name="keywords" content="Auto Classifier models, node scripting properties, applyautoclassifiernode... |
E399A5B6FA720C6F21337792F822F20F20F98910 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/autoclusternodeslots.html?context=cdpaas&locale=en | autoclusternode properties | autoclusternode properties
The Auto Cluster node estimates and compares clustering models, which identify groups of records that have similar characteristics. The node works i... | # autoclusternode properties #
The Auto Cluster node estimates and compares clustering models, which identify groups of records that have similar characteristics\. The node work... | <!doctype html>
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="keywords" content="Auto Cluster node, node scripting properties, autoclusternode properties">... |
14416203D840C788359110B18CFD9CE922DE0D67 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/autoclusternuggetnodeslots.html?context=cdpaas&locale=en | applyautoclusternode properties | applyautoclusternode properties
You can use Auto Cluster modeling nodes to generate an Auto Cluster model nugget. The scripting name of this model nugget is applyautoclusternode. No other properties exist for this model nugget. For more information on scripting the modeling node itself, see [autoclusternode properti... | # applyautoclusternode properties #
You can use Auto Cluster modeling nodes to generate an Auto Cluster model nugget\. The scripting name of this model nugget is *applyautoclusternode*\. No other properties exist for this model nugget\. For more information on scripting the modeling node itself, see [autoclusternode p... | <!doctype html>
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="keywords" content="Auto Cluster models, node scripting properties, applyautoclusternode prope... |
3EAAFDDADE769D3B0300BE1401BB3D7E68B312DD | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/autonumericnuggetnodeslots.html?context=cdpaas&locale=en | applyautonumericnode properties | applyautonumericnode properties
You can use Auto Numeric modeling nodes to generate an Auto Numeric model nugget. The scripting name of this model nugget is applyautonumericnode.For more information on scripting the modeling node itself, see [autonumericnode properties](https://dataplatform.cloud.ibm.com/docs/conten... | # applyautonumericnode properties #
You can use Auto Numeric modeling nodes to generate an Auto Numeric model nugget\. The scripting name of this model nugget is *applyautonumericnode*\.For more information on scripting the modeling node itself, see [autonumericnode properties](https://dataplatform.cloud.ibm.com/docs/... | <!doctype html>
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<meta name="keywords" content="Auto Numeric models, node scripting properties, applyautonumericnode prope... |
D2D9F4E05CABC566B2021116ED28EF413FA96779 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/available_slot_parameters.html?context=cdpaas&locale=en | Node properties overview | Node properties overview
Each type of node has its own set of legal properties, and each property has a type. This type may be a general type—number, flag, or string—in which case settings for the property are coerced to the correct type. An error is raised if they can't be coerced. Alternatively, the property refer... | # Node properties overview #
Each type of node has its own set of legal properties, and each property has a type\. This type may be a general type—number, flag, or string—in which case settings for the property are coerced to the correct type\. An error is raised if they can't be coerced\. Alternatively, the property ... | <!doctype html>
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7A9F4CDF362D1F06C3644EDBD634B2A77DDC6005 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/balancenodeslots.html?context=cdpaas&locale=en | balancenode properties | balancenode properties
 The Balance node corrects imbalances in a dataset, so it conforms to a specified condition. The balancing directive adjusts the proportion of records where a co... | # balancenode properties #
 The Balance node corrects imbalances in a dataset, so it conforms to a specified condition\. The balancing directive adjusts the proportion of records where a... | <!doctype html>
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FE2254205E6DD1EE2A4EC62036AB86BC5E084F5D | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/bayesnetnodeslots.html?context=cdpaas&locale=en | bayesnetnode properties | bayesnetnode properties
With the Bayesian Network (Bayes Net) node, you can build a probability model by combining observed and recorded evidence with real-world knowledge to e... | # bayesnetnode properties #
With the Bayesian Network (Bayes Net) node, you can build a probability model by combining observed and recorded evidence with real\-world knowledge t... | <!doctype html>
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EC154AE6F7FE894644424BFA90C6CA31E13A4B71 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/bayesnetnuggetnodeslots.html?context=cdpaas&locale=en | applybayesnetnode properties | applybayesnetnode properties
You can use Bayesian network modeling nodes to generate a Bayesian network model nugget. The scripting name of this model nugget is applybayesnetnode. For more information on scripting the modeling node itself, see [bayesnetnode properties](https://dataplatform.cloud.ibm.com/docs/content... | # applybayesnetnode properties #
You can use Bayesian network modeling nodes to generate a Bayesian network model nugget\. The scripting name of this model nugget is *applybayesnetnode*\. For more information on scripting the modeling node itself, see [bayesnetnode properties](https://dataplatform.cloud.ibm.com/docs/c... | <!doctype html>
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CDA0897D49B56EE521BF16E52014DA5E2E1D2710 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/binaryclassifiernodeslots.html?context=cdpaas&locale=en | autoclassifiernode properties | autoclassifiernode properties
The Auto Classifier node creates and compares a number of different models for binary outcomes (yes or no, churn or do not churn, and so ... | # autoclassifiernode properties #
The Auto Classifier node creates and compares a number of different models for binary outcomes (yes or no, churn or do not churn, and s... | <!doctype html>
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B741FE5CDD06D606F869B15DEB2173C1F134D22D | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/binningnodeslots.html?context=cdpaas&locale=en | binningnode properties | binningnode properties
The Binning node automatically creates new nominal (set) fields based on the values of one or more existing continuous (numeric range) fields. For example, you c... | # binningnode properties #
The Binning node automatically creates new nominal (set) fields based on the values of one or more existing continuous (numeric range) fields\. For example, yo... | <!doctype html>
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5C95F2D19465DDA8969D0498D1B96D870BD02A1F | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/c50nodeslots.html?context=cdpaas&locale=en | c50node properties | c50node properties
The C5.0 node builds either a decision tree or a rule set. The model works by splitting the sample based on the field that provides the maximum information gain at each lev... | # c50node properties #
The C5\.0 node builds either a decision tree or a rule set\. The model works by splitting the sample based on the field that provides the maximum information gain at eac... | <!doctype html>
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FCBDBFD3E4BEBEFE552FAD012509948FABA34B44 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/c50nuggetnodeslots.html?context=cdpaas&locale=en | applyc50node properties | applyc50node properties
You can use C5.0 modeling nodes to generate a C5.0 model nugget. The scripting name of this model nugget is applyc50node. For more information on scripting the modeling node itself, see [c50node properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/c... | # applyc50node properties #
You can use C5\.0 modeling nodes to generate a C5\.0 model nugget\. The scripting name of this model nugget is *applyc50node*\. For more information on scripting the modeling node itself, see [c50node properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clem... | <!doctype html>
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<meta ... |
499553788712E55ABE1345C61CCDB15D1CE04E83 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/carmanodeslots.html?context=cdpaas&locale=en | carmanode properties | carmanode properties
The CARMA model extracts a set of rules from the data without requiring you to specify input or target fields. In contrast to Apriori, the CARMA node offers build setti... | # carmanode properties #
The CARMA model extracts a set of rules from the data without requiring you to specify input or target fields\. In contrast to Apriori, the CARMA node offers build s... | <!doctype html>
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CE14B5EFF03A17683C6AA16D02F62E1EBAD0D7F2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/carmanuggetnodeslots.html?context=cdpaas&locale=en | applycarmanode properties | applycarmanode properties
You can use Carma modeling nodes to generate a Carma model nugget. The scripting name of this model nugget is applycarmanode. For more information on scripting the modeling node itself, see [carmanode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clem... | # applycarmanode properties #
You can use Carma modeling nodes to generate a Carma model nugget\. The scripting name of this model nugget is *applycarmanode*\. For more information on scripting the modeling node itself, see [carmanode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guid... | <!doctype html>
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<me... |
CB130D4E1AE505CE39CBD49BF9D22359B9EC80AB | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/cartnodeslots.html?context=cdpaas&locale=en | cartnode properties | cartnode properties
The Classification and Regression (C&R) Tree node generates a decision tree that allows you to predict or classify future observations. The method uses recursive part... | # cartnode properties #
The Classification and Regression (C&R) Tree node generates a decision tree that allows you to predict or classify future observations\. The method uses recursive p... | <!doctype html>
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<meta ... |
C53BD428F2955B76BF24620A21A6461A1CC19F11 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/cartnuggetnodeslots.html?context=cdpaas&locale=en | applycartnode properties | applycartnode properties
You can use C&R Tree modeling nodes to generate a C&R Tree model nugget. The scripting name of this model nugget is applycartnode. For more information on scripting the modeling node itself, see [cartnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/c... | # applycartnode properties #
You can use C&R Tree modeling nodes to generate a C&R Tree model nugget\. The scripting name of this model nugget is *applycartnode*\. For more information on scripting the modeling node itself, see [cartnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_g... | <!doctype html>
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<... |
B0B1665F022C9E781CE1AE94FA885266391FBCFE | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/chaidnodeslots.html?context=cdpaas&locale=en | chaidnode properties | chaidnode properties
The CHAID node generates decision trees using chi-square statistics to identify optimal splits. Unlike the C&R Tree and Quest nodes, CHAID can generate non-binary tree... | # chaidnode properties #
The CHAID node generates decision trees using chi\-square statistics to identify optimal splits\. Unlike the C&R Tree and Quest nodes, CHAID can generate non\-binary... | <!doctype html>
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<meta name="keywords" content="CHAID models, node scripting properties, chaidnode properties">
<meta na... |
6644EAA4A383F7ED21C0CA1ADAE80A634867870A | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/chaidnuggetnodeslots.html?context=cdpaas&locale=en | applychaidnode properties | applychaidnode properties
You can use CHAID modeling nodes to generate a CHAID model nugget. The scripting name of this model nugget is applychaidnode. For more information on scripting the modeling node itself, see [chaidnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clem... | # applychaidnode properties #
You can use CHAID modeling nodes to generate a CHAID model nugget\. The scripting name of this model nugget is *applychaidnode*\. For more information on scripting the modeling node itself, see [chaidnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guid... | <!doctype html>
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<meta name="keywords" content="CHAID models, node scripting properties, applychaidnode properties">
<me... |
FD45693344E2B3CC3BDB7D1AA209AD9FBACB5309 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/chartnodeslots.html?context=cdpaas&locale=en | dvcharts properties | dvcharts properties
With the Charts node, you can launch the chart builder and create chart definitions to save with your flow. Then when you run the node, chart output is generated.
... | # dvcharts properties #
With the Charts node, you can launch the chart builder and create chart definitions to save with your flow\. Then when you run the node, chart output is generated\.... | <!doctype html>
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<meta name="keywords" content="Charts node, properties, chartsnode properties">
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F24C445F7AB9052A92E411B826C60DEE2DF78448 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/collectionnodeslots.html?context=cdpaas&locale=en | collectionnode properties | collectionnode properties
The Collection node shows the distribution of values for one numeric field relative to the values of another. (It creates graphs that are similar to his... | # collectionnode properties #
The Collection node shows the distribution of values for one numeric field relative to the values of another\. (It creates graphs that are similar to ... | <!doctype html>
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F1B21B1232720492424BB07CD73C93DF2B9CD229 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/coxregnodeslots.html?context=cdpaas&locale=en | coxregnode properties | coxregnode properties
The Cox regression node enables you to build a survival model for time-to-event data in the presence of censored records. The model produces a survival function tha... | # coxregnode properties #
The Cox regression node enables you to build a survival model for time\-to\-event data in the presence of censored records\. The model produces a survival functio... | <!doctype html>
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<meta name="keywords" content="Cox regression models, node scripting properties, coxregnode properties">
... |
CEBDC984A6E14E7DC6B7526324BF06A0CE6FFE34 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/coxregnuggetnodeslots.html?context=cdpaas&locale=en | applycoxregnode properties | applycoxregnode properties
You can use Cox modeling nodes to generate a Cox model nugget. The scripting name of this model nugget is applycoxregnode. For more information on scripting the modeling node itself, see [coxregnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/cleme... | # applycoxregnode properties #
You can use Cox modeling nodes to generate a Cox model nugget\. The scripting name of this model nugget is *applycoxregnode*\. For more information on scripting the modeling node itself, see [coxregnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide... | <!doctype html>
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<meta name="dcterms.rights" content="© Copyright IBM Corporation 2023">
<meta name="keywords" content="Cox regression models, node scripting properties, applycoxregnode properti... |
7566F3896A5AC6F89F4E7E18DC21B4A6A63864B4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/cplexnodeslots.html?context=cdpaas&locale=en | cplexoptnode properties | cplexoptnode properties
 The CPLEX Optimization node provides the ability to use complex mathematical (CPLEX) based optimization via an Optimization Programming Language (OPL)... | # cplexoptnode properties #
 The CPLEX Optimization node provides the ability to use complex mathematical (CPLEX) based optimization via an Optimization Programming Language (OP... | <!doctype html>
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02D819D225558542A49AB6E43F94FE062A509EA5 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/dataassetexportnodeslots.html?context=cdpaas&locale=en | dataassetexport properties | dataassetexport properties
You can use the Data Asset Export node to write to remove data sources using connections, write to a data file on your local computer, or write... | # dataassetexport properties #
You can use the Data Asset Export node to write to remove data sources using connections, write to a data file on your local computer, or wri... | <!doctype html>
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46915AFE957CA00C5B825C5F2BDC618BFEA43DE8 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/dataassetimportnodeslots.html?context=cdpaas&locale=en | dataassetimport properties | dataassetimport properties
 You can use the Data Asset import node to pull in data from remote data sources using connections or from your local computer.
dataassetimpor... | # dataassetimport properties #
 You can use the Data Asset import node to pull in data from remote data sources using connections or from your local computer\.
<!-- <table "... | <!doctype html>
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