doc_id string | url string | title string | text string | label string | label_id int64 | split string |
|---|---|---|---|---|---|---|
AAC6535CAB0B4600A9683433FCAB805B2C4EAA53 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/structured_slot_parameters.html?context=cdpaas&locale=en | Structured properties | Structured properties
Structured properties There are two ways in which scripting uses structured properties for increased clarity when parsing: * To give structure to the names of properties for complex nodes, such as Type, Filter, or Balance nodes. * To provide a format for specifying multiple properties at once. | conceptual | 0 | train |
78488A77CB39BDD413DBB7682F1DBE2675B3E3A0 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/jython/clementine/python_create_class.html?context=cdpaas&locale=en | Defining a class | Defining a class
Defining a class Within a Python class, you can define both variables and methods. Unlike in Java, in Python you can define any number of public classes per source file (or module). Therefore, you can think of a module in Python as similar to a package in Java. In Python, classes are defined using the ... | how-to | 1 | train |
FB7F7B9A220C66F7E3407CA9553D974CD4A14402 | https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-manage-feedback-data.html?context=cdpaas&locale=en | Managing feedback data for watsonx.governance | Managing feedback data for watsonx.governance
Managing feedback data for watsonx.governance You must provide feedback data to watsonx.governance to enable you to configure quality and generative AI quality evaluations and determine any changes in your model predictions. When you provide feedback data to watsonx.governa... | how-to | 1 | train |
835B998310E6E268F648D4AA28528190EBBB48CA | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/extension_pyspark_examples.html?context=cdpaas&locale=en | Examples (SPSS Modeler) | Examples (SPSS Modeler)
Examples This section provides Python for Spark scripting examples. | conceptual | 0 | train |
97C26F347FD5A13FBC5B24FC567FCF7ADF8CE0C3 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-create-model.html?context=cdpaas&locale=en | Creating your own models | Creating your own models
Creating your own models Certain algorithms in Watson Natural Language Processing can be trained with your own data, for example you can create custom models based on your own data for entity extraction, to classify data, to extract sentiments, and to extract target sentiments. Starting with Ru... | how-to | 1 | train |
093BFFCB43C46F1068A59A6B6338C955BF20AABF | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/multiplotnodeslots.html?context=cdpaas&locale=en | multiplotnode properties | multiplotnode properties
multiplotnode properties The Multiplot node creates a plot that displays multiple Y fields over a single X field. The Y fields are plotted as colored lines; e... | conceptual | 0 | train |
DEB599F49C3E459A08E8BF25304B063B50CAA294 | https://dataplatform.cloud.ibm.com/docs/content/DO/WML_Deployment/DeployModelUI-WML.html?context=cdpaas&locale=en | Deploying a Decision Optimization model by using the user interface | Deploying a Decision Optimization model by using the user interface
Deploying a Decision Optimization model by using the user interface You can save a model for deployment in the Decision Optimization experiment UI and promote it to your Watson Machine Learning deployment space. Procedure To save your model for deploym... | how-to | 1 | train |
CE13AE6812F1E2CA6AD429D4B01AF25F9F398148 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-overview.html?context=cdpaas&locale=en | Deploying models with Watson Machine Learning | Deploying models with Watson Machine Learning
Deploying models with Watson Machine Learning Using IBM Watson Machine Learning, you can deploy models, scripts, and functions, manage your deployments, and prepare your assets to put into production to generate predictions and insights. This graphic illustrates a typical p... | how-to | 1 | train |
A1365CD1E2ACBEE6E9BF025DD493FEB17A0D428F | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/tmwb_advanced_linguistic.html?context=cdpaas&locale=en | Advanced linguistic settings (SPSS Modeler) | Advanced linguistic settings (SPSS Modeler)
Advanced linguistic settings When you build categories, you can select from a number of advanced linguistic category building techniques such as concept inclusion and semantic networks (English text only). These techniques can be used individually or in combination with each ... | conceptual | 0 | train |
0310B7FB9072E7F7E5D73F5AF90EDE62FAA81286 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-hardware-configs.html?context=cdpaas&locale=en | Managing hardware configurations | Managing hardware configurations
Managing hardware configurations When you deploy certain assets in Watson Machine Learning, you can choose the type, size, and power of the hardware configuration that matches your computing needs. Deployment types that require hardware specifications Selecting a hardware specification ... | how-to | 1 | train |
577964B0C132F5EA793054C3FF67417DDA6511D3 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-samples-overview.html?context=cdpaas&locale=en | Watson Machine Learning Python client samples and examples | Watson Machine Learning Python client samples and examples
Watson Machine Learning Python client samples and examples Review and use sample Jupyter Notebooks that use Watson Machine Learning Python library to demonstrate machine learning features and techniques. Each notebook lists learning goals so you can find the on... | how-to | 1 | train |
277C8CB678CAF766466EDE03C506EB0A822FD400 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdpaas&locale=en | Supported data sources in Decision Optimization | Supported data sources in Decision Optimization
Supported data sources in Decision Optimization Decision Optimization supports the following relational and nonrelational data sources on . watsonx.ai. * [IBM data sources](https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOconnections.html?context=cdp... | conceptual | 0 | train |
3EAAFDDADE769D3B0300BE1401BB3D7E68B312DD | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/autonumericnuggetnodeslots.html?context=cdpaas&locale=en | applyautonumericnode properties | 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://dataplatf... | conceptual | 0 | train |
0999F59BB8E2E2AB7722D57CDBC051A0984ABE45 | https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/data_flows.html?context=cdpaas&locale=en | Managing Data Refinery flows | Managing Data Refinery flows
Managing Data Refinery flows A Data Refinery flow is an ordered set of steps to cleanse, shape, and enhance data. As you [refine your data](https://dataplatform.cloud.ibm.com/docs/content/wsj/refinery/refining_data.htmlrefine) by [applying operations](https://dataplatform.cloud.ibm.com/docs... | how-to | 1 | train |
870BF64E17FEB1BBDAE7B35E9941DB781F26AD6B | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-pipeline.html?context=cdpaas&locale=en | Quick start: Automate the lifecycle for a model with pipelines | Quick start: Automate the lifecycle for a model with pipelines
Quick start: Automate the lifecycle for a model with pipelines You can create an end-to-end pipeline to deliver concise, pre-processed, and up-to-date data stored in an external data source. Read about Watson Pipelines, then watch a video and take a tutoria... | how-to | 1 | train |
6F51A9033343574AEE2D292CB23F09D542456389 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-model-tracking.html?context=cdpaas&locale=en | Enabling model tracking with AI factsheets | Enabling model tracking with AI factsheets
Enabling model tracking with AI factsheets If your organization is using AI Factsheets as part of an AI governance strategy, you can track models after adding them to a space. Tracking a model populates a factsheet in an associated model use case. The model use cases are maint... | how-to | 1 | train |
BF6A65F061558B6AED8A438A887B6474A0FDFFC3 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/report.html?context=cdpaas&locale=en | Report node (SPSS Modeler) | 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 formatt... | conceptual | 0 | train |
3CF77633A489E42B01086588D6613D65BFD51F7F | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_intro_score.html?context=cdpaas&locale=en | Scoring records (SPSS Modeler) | Scoring records (SPSS Modeler)
Scoring records Earlier, we scored the same records used to estimate the model so we could evaluate how accurate the model was. Now we'll score a different set of records from the ones used to create the model. This is the goal of modeling with a target field: Study records for which you ... | conceptual | 0 | train |
F37BD72C28F0DAC8D9478ECEABA4F077ABCDE0C9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Notebooks/createScenario.html?context=cdpaas&locale=en | Decision Optimization notebook tutorial create new scenario | Decision Optimization notebook tutorial create new scenario
Create new scenario To solve with different versions of your model or data you can create new scenarios in the Decision Optimization experiment UI. Procedure To create a new scenario: 1. Click the Open scenario pane icon  | Fields (SPSS Modeler)
Fields Names in CLEM expressions that aren’t names of functions are assumed to be field names. You can write these simply as Power, val27, state_flag, and so on, but if the name begins with a digit or includes non-alphabetic characters, such as spaces (with the exception of the underscore), place ... | conceptual | 0 | train |
2D08EDD168FBEE078290F386F7EC3EB1998ADF02 | https://dataplatform.cloud.ibm.com/docs/content/wsj/spark/time-reference-system.html?context=cdpaas&locale=en | Time reference system | Time reference system
Time reference system Time reference system (TRS) is a local, regional or global system used to identify time. A time reference system defines a specific projection for forward and reverse mapping between a timestamp and its numeric representation. A common example that most users are familiar wit... | how-to | 1 | train |
7A9F4CDF362D1F06C3644EDBD634B2A77DDC6005 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/balancenodeslots.html?context=cdpaas&locale=en | balancenode properties | 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 o... | conceptual | 0 | train |
C535650C17CDE010EACBF5B6BF85FD8E593B77D6 | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-do.html?context=cdpaas&locale=en | Quick start: Build, run, and deploy a Decision Optimization model | Quick start: Build, run, and deploy a Decision Optimization model
Quick start: Build, run, and deploy a Decision Optimization model You can build and run Decision Optimization models to help you make the best decisions to solve business problems based on your objectives. Read about Decision Optimization, then watch a v... | how-to | 1 | train |
18A7A354C4B46E26DF8304755C8BE954BB922B04 | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_drug_browse.html?context=cdpaas&locale=en | Browsing a model (SPSS Modeler) | Browsing a model (SPSS Modeler)
Browsing the model When the C5.0 node runs, its model nugget is added to the flow. To browse the model, right-click the model nugget and choose View Model. The Tree Diagram displays the set of rules generated by the C5.0 node in a tree format. Now you can see the missing pieces of the pu... | conceptual | 0 | train |
9D9C67189BE5D6DB22575CF01A75BD5826B92074 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/autonumeric.html?context=cdpaas&locale=en | Auto Numeric node (SPSS Modeler) | Auto Numeric node (SPSS Modeler)
Auto Numeric node The Auto Numeric node estimates and compares models for continuous numeric range outcomes using a number of different methods, enabling you to try out a variety of approaches in a single modeling run. You can select the algorithms to use, and experiment with multiple c... | conceptual | 0 | train |
DE6C4CB72844FC59FD80FC0B26ACC8C94A3BA994 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/cache_nodes.html?context=cdpaas&locale=en | Caching options for nodes (SPSS Modeler) | Caching options for nodes (SPSS Modeler)
Caching options for nodes To optimize the running of flows, you can set up a cache on any nonterminal node. When you set up a cache on a node, the cache is filled with the data that passes through the node the next time you run the data flow. From then on, the data is read from ... | how-to | 1 | train |
6576530EC5D705B8BF323F6C459C32A87AE3F9A4 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/mlpas.html?context=cdpaas&locale=en | MultiLayerPerceptron-AS node (SPSS Modeler) | 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.a... | conceptual | 0 | train |
F4F623D5A7C8913E227E962BD1F347B36AAB7B51 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/clem_reference/expressions_and_conditions.html?context=cdpaas&locale=en | Expressions and conditions (SPSS Modeler) | Expressions and conditions (SPSS Modeler)
Expressions and conditions CLEM expressions can return a result (used when deriving new values). For example: Weight * 2.2 Age + 1 sqrt(Signal-Echo) Or, they can evaluate true or false (used when selecting on a condition). For example: Drug = "drugA" Age < 16 not(PowerFlux) and... | conceptual | 0 | train |
03A70C271775C3B15541B86E53E467844EF87296 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/jython/clementine/python_syntax_remarks.html?context=cdpaas&locale=en | Remarks | Remarks
Remarks Remarks are comments that are introduced by the pound (or hash) sign (). All text that follows the pound sign on the same line is considered part of the remark and is ignored. A remark can start in any column. The following example demonstrates the use of remarks: The HelloWorld application is one of th... | conceptual | 0 | train |
CB81643BE8EE3B3DC2F6BCCDB77BD2CEC32C8926 | https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/int-google.html?context=cdpaas&locale=en | Integrating with Google Cloud Platform | Integrating with Google Cloud Platform
Integrating with Google Cloud Platform You can configure an integration with the Google Cloud Platform (GCP) to allow IBM watsonx users to access data sources from GCP. Before proceeding, make sure you have proper permissions. After you configure an integration, you'll see it unde... | how-to | 1 | train |
EC10AC085BA8A12BA0D8AF2DC66ADFBE759B3183 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/simgen.html?context=cdpaas&locale=en | Sim Gen node (SPSS Modeler) | Sim Gen node (SPSS Modeler)
Sim Gen node The Simulation Generate node provides an easy way to generate simulated data, either without historical data using user specified statistical distributions, or automatically using the distributions obtained from running a Simulation Fitting node on existing historical data. Gene... | conceptual | 0 | train |
A3022FF9DB2732F0AB3091884B428763D3879FD2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_intro_build.html?context=cdpaas&locale=en | Building the flow (SPSS Modeler) | Building the flow (SPSS Modeler)
Building the flow Figure 1. Modeling flow  To build a flow that will create a model, we need at least three elements: * A Data Asset node that reads in data from an external source, in t... | how-to | 1 | train |
02D819D225558542A49AB6E43F94FE062A509EA5 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/dataassetexportnodeslots.html?context=cdpaas&locale=en | dataassetexport properties | 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 ... | conceptual | 0 | train |
5E4D2166BB8C2B95E515591E014E7CA00B87BCA2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_ta_hotel_iwb.html?context=cdpaas&locale=en | Using the Text Analytics Workbench (SPSS Modeler) | Using the Text Analytics Workbench (SPSS Modeler)
Using the Text Analytics Workbench The Text Analytics Workbench contains the extraction results and the category model contained in the text analytics package. | conceptual | 0 | train |
B7E56BEBF29F9AA59A9ABC9E299F19613E5859DA | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/twostepAS.html?context=cdpaas&locale=en | TwoStep-AS cluster node (SPSS Modeler) | TwoStep-AS cluster node (SPSS Modeler)
TwoStep-AS cluster node TwoStep Cluster is an exploratory tool that is designed to reveal natural groupings (or clusters) within a data set that would otherwise not be apparent. The algorithm that is employed by this procedure has several desirable features that differentiate it f... | conceptual | 0 | train |
114EBF33612531C5020FD739010049E5126E0E5B | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/xgboostas.html?context=cdpaas&locale=en | XGBoost-AS node (SPSS Modeler) | XGBoost-AS node (SPSS Modeler)
XGBoost-AS node XGBoost© is an advanced implementation of a gradient boosting algorithm. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier. XGBoost is very flexible and provides many parameters that can be overwhelming to most users, so ... | conceptual | 0 | train |
6E50438308B85E969B79DED22CC5E15F6872EE85 | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_autocont.html?context=cdpaas&locale=en | Automated modeling for a continuous target (SPSS Modeler) | Automated modeling for a continuous target (SPSS Modeler)
Automated modeling for a continuous target You can use the Auto Numeric node to automatically create and compare different models for continuous (numeric range) outcomes, such as predicting the taxable value of a property. With a single node, you can estimate an... | conceptual | 0 | train |
5466D9A71E87BB01000DC957683E9CD3C10AD8BC | https://dataplatform.cloud.ibm.com/docs/content/dataview/chart_creation_bubble.html?context=cdpaas&locale=en | Bubble charts | Bubble charts
Bubble charts Bubble charts display categories in your groups as nonhierarchical packed circles. The size of each circle (bubble) is proportional to its value. Bubble charts are useful for comparing relationships in your data. | conceptual | 0 | train |
8BC347015FD7CE2AF13B17DE4D287471CB994F38 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/jython/clementine/python_api_intro.html?context=cdpaas&locale=en | The scripting API | The scripting API
The scripting API The Scripting API provides access to a wide range of SPSS Modeler functionality. All the methods described so far are part of the API and can be accessed implicitly within the script without further imports. However, if you want to reference the API classes, you must import the API e... | conceptual | 0 | train |
E990E009903E315FA6752E7E82C2634AF4A425B9 | https://dataplatform.cloud.ibm.com/docs/content/DO/DODS_Introduction/DOintro.html?context=cdpaas&locale=en | Ways to use Decision Optimization | Ways to use Decision Optimization
Ways to use Decision Optimization To build Decision Optimization models, you can create Python notebooks with DOcplex, a native Python API for Decision Optimization, or use the Decision Optimization experiment UI that has more benefits and features. | conceptual | 0 | train |
97B722619AFC616F13BEB20CD7A8FBC29CFF50D1 | https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/job-views-projs.html?context=cdpaas&locale=en | Viewing jobs across projects | Viewing jobs across projects
Viewing jobs across projects You can view the jobs that exist across projects for assets that run in tools, such as notebooks, Data Refinery flows, and SPSS Modeler flows. To view the status of jobs or job runs in projects: 1. From the navigation menu, select Projects > Jobs. 2. Select a vi... | how-to | 1 | train |
9346A72CFCD74DFDA05213A2A321BF9CFB823358 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/apriori.html?context=cdpaas&locale=en | Apriori node (SPSS Modeler) | Apriori node (SPSS Modeler)
Apriori node The Apriori node discovers association rules in your data. Association rules are statements of the form: if antecedent(s) then consequent(s) For example, if a customer purchases a razor and after shave, then that customer will purchase shaving cream with 80% confidence. Apriori ... | conceptual | 0 | train |
27E7AD16129A9DC8AC8CE2EE79C9B584D441F0DE | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/scripting_accessresults.html?context=cdpaas&locale=en | Accessing flow run results | Accessing flow run results
Accessing flow run results Many SPSS Modeler nodes produce output objects such as models, charts, and tabular data. Many of these outputs contain useful values that can be used by scripts to guide subsequent runs. These values are grouped into content containers (referred to as simply contain... | how-to | 1 | train |
85E9CAC1F581E61092CFF1F6BE38570EE734C115 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-space-export.html?context=cdpaas&locale=en | Exporting space assets from deployment spaces | Exporting space assets from deployment spaces
Exporting space assets from deployment spaces You can export assets from a deployment space so that you can share the space with others or reuse the assets in another space. For a list of assets that you can export from space, refer to [Assets in a deployment space](https:/... | how-to | 1 | train |
8109B6380043CE464115025DD32A7A821FD56DB7 | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-tuning-studio.html?context=cdpaas&locale=en | Quick start: Tune a foundation model | Quick start: Tune a foundation model
Quick start: Tune a foundation model There are a couple of reasons to tune your foundation model. By tuning a model on many labeled examples, you can enhance the model performance compared to prompt engineering alone. By tuning a base model to perform similarly to a bigger model in ... | how-to | 1 | train |
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 (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. | conceptual | 0 | train |
FC8DBF139A485E98914CBB73B8BA684B283AE983 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-tuning-deploy.html?context=cdpaas&locale=en | Deploying a tuned foundation model | Deploying a tuned foundation model
Deploying a tuned foundation model Deploy a tuned model so you can add it to a business workflow and start to use foundation models in a meaningful way. Before you begin The tuning experiment that you used to tune the foundation model must be finished. For more information, see [Tunin... | how-to | 1 | train |
F0EF147DBC0554F53B331E7B6D5715D0269FFBA8 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/jython/clementine/python_node_reference.html?context=cdpaas&locale=en | Referencing existing nodes | Referencing existing nodes
Referencing existing nodes A flow is often pre-built with some parameters that must be modified before the flow runs. Modifying these parameters involves the following tasks: 1. Locating the nodes in the relevant flow. 2. Changing the node or flow settings (or both). | conceptual | 0 | train |
784686DA695F28F867BC35C4416CB8D767D58B7A | https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/manage-assets.html?context=cdpaas&locale=en | Managing assets in projects | Managing assets in projects
Managing assets in projects You can manage assets in a project by adding them, editing them, or deleting them. * [Add data assets](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/add-data-project.html) * You can add other types of assets by clicking New asset or Import assets... | how-to | 1 | train |
D669435B8D1C91D913BD24768E52644B95C675AE | https://dataplatform.cloud.ibm.com/docs/content/wsj/ai-risk-atlas/unreliable-source-attribution.html?context=cdpaas&locale=en | {{ document.title.text }} | {{ document.title.text }}
Unreliable source attribution Risks associated with outputExplainabilityAmplified Description Source attribution is the AI system's ability to describe from what tra... | conceptual | 0 | train |
22B8136F68AC74838B9C2B9EAF3996CCFAA14921 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/transpose.html?context=cdpaas&locale=en | Transpose node (SPSS Modeler) | Transpose node (SPSS Modeler)
Transpose node By default, columns are fields and rows are records or observations. If necessary, you can use a Transpose node to swap the data in rows and columns so that fields become records and records become fields. For example, if you have time series data where each series is a row ... | conceptual | 0 | train |
67B99E436854F015A9DB19C775639BA4BB4D5F9B | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/cplex.html?context=cdpaas&locale=en | CPLEX Optimization node (SPSS Modeler) | CPLEX Optimization node (SPSS Modeler)
CPLEX Optimization node With the CPLEX Optimization node, you can use complex mathematical (CPLEX) based optimization via an Optimization Programming Language (OPL) model file. For more information about CPLEX optimization and OPL, see the [IBM ILOG CPLEX Optimization Studio docum... | conceptual | 0 | train |
F3C0AD81BBF56463510440F7F81EB146A6C0015C | https://dataplatform.cloud.ibm.com/docs/content/wsj/spark/time-series-lazy-evaluation.html?context=cdpaas&locale=en | Time series lazy evaluation | Time series lazy evaluation
Time series lazy evaluation Lazy evaluation is an evaluation strategy that delays the evaluation of an expression until its value is needed. When combined with memoization, lazy evaluation strategy avoids repeated evaluations and can reduce the running time of certain functions by a signific... | how-to | 1 | train |
0A507FF5262BAD7A3FB3F3C478388CFF78949941 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/py_assetframe.html?context=cdpaas&locale=en | Managing feature groups with assetframe-lib for Python (beta) | Managing feature groups with assetframe-lib for Python (beta)
Managing feature groups with assetframe-lib for Python (beta) You can use the assetframe-lib to create, view and edit feature group information for data assets in Watson Studio notebooks. Feature groups define additional metadata on columns of your data asse... | how-to | 1 | train |
2200315EA9DA921EDFF8A3322417BB211F15B4EB | https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/connected-data.html?context=cdpaas&locale=en | Adding data from a connection to a project | Adding data from a connection to a project
Adding data from a connection to a project A connected data asset is a pointer to data that is accessed through a connection to an external data source. You create a connected data asset by specifying a connection, any intermediate structures or paths, and a relational table o... | how-to | 1 | train |
FD48879C34D316981B4F67C2B82C8179E0042F74 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-credentials.html?context=cdpaas&locale=en | Credentials for prompting foundation models (IBM Cloud API key and IAM token) | Credentials for prompting foundation models (IBM Cloud API key and IAM token)
Credentials for prompting foundation models (IBM Cloud API key and IAM token) To prompt foundation models in IBM watsonx.ai programmatically, you need an IBM Cloud API key and sometimes an IBM Cloud IAM token. IBM Cloud API key To use the [fo... | how-to | 1 | train |
A73CA4F67523DBB58FD3521AE9BFF83AEE634607 | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_drug_distribution.html?context=cdpaas&locale=en | Creating a distribution chart (SPSS Modeler) | Creating a distribution chart (SPSS Modeler)
Creating a distribution chart During data mining, it is often useful to explore the data by creating visual summaries. Watson Studio offers many different types of charts to choose from, depending on the kind of data you want to summarize. For example, to find out what propo... | how-to | 1 | train |
E334A64775AE571C661CDCC847669F0E20C207FF | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/videos-wx.html?context=cdpaas&locale=en | Video library | Video library
Video library Watch short videos for data scientists, data engineers, and data stewards to learn about watsonx. The videos and accompanying tutorials are task-focused and provide hands-on experience by using the tools in watsonx. Note: These videos provides a visual method to learn the concepts and tasks ... | conceptual | 0 | train |
40D279ADA16512E67B7FB78FDAC4ADA9CFE5C645 | https://dataplatform.cloud.ibm.com/docs/content/wsj/ai-risk-atlas/data-provenance.html?context=cdpaas&locale=en | {{ document.title.text }} | {{ document.title.text }}
Data provenance Risks associated with inputTraining and tuning phaseTransparencyAmplified Description Without standardized and established methods for verifying where da... | conceptual | 0 | train |
3A81B302EE01FDC0AC111CFF3ABFDB96E3A0CDD6 | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/security-overview.html?context=cdpaas&locale=en | Security for IBM watsonx | Security for IBM watsonx
Security for IBM watsonx Security mechanisms in IBM watsonx provide protection for data, applications, identity, and resources. You can configure security mechanisms on five levels for IBM Cloud security functions. Security levels in IBM watsonx Security for IBM watsonx is configured on levels ... | conceptual | 0 | train |
19BA0BFC40B6212B42F38487F1533BB65647850E | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-importing-model.html?context=cdpaas&locale=en | Importing models to a deployment space | Importing models to a deployment space
Importing models to a deployment space Import machine learning models trained outside of IBM Watson Machine Learning so that you can deploy and test the models. Review the model frameworks that are available for importing models. Here, to import a trained model means: 1. Store the... | how-to | 1 | train |
538ECAE0B5AA21E499F39C2637764A05BFF7B6B6 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-manage-reports.html?context=cdpaas&locale=en | Managing and customizing report templates | Managing and customizing report templates
Managing and customizing report templates If the default report templates that are provided with AI Factsheets do not meet your needs, you can download a default report template, customize it, and upload the new template. Customizing a custom report template Any user with at le... | how-to | 1 | train |
5A6081124D93ACD0A12843F64984257A02BB3871 | https://dataplatform.cloud.ibm.com/docs/content/wsj/troubleshoot/troubleshoot-conn.html?context=cdpaas&locale=en | Troubleshooting connections | Troubleshooting connections
Troubleshooting connections Use these solutions to resolve problems that you might encounter with connections. IBM Db2 for z/OS: Error retrieving the schema list when you try to connect to a Db2 for z/OS server When you test the connection to a Db2 for z/OS server and the connection cannot r... | how-to | 1 | train |
05275F4EC521878B13AD7DCE825E167B2FC7EF93 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/tmwb_advanced_frequencies.html?context=cdpaas&locale=en | Advanced frequency settings (SPSS Modeler) | Advanced frequency settings (SPSS Modeler)
Advanced frequency settings You can build categories based on a straightforward and mechanical frequency technique. With this technique, you can build one category for each item (type, concept, or pattern) that was found to be higher than a given record or document count. Addi... | conceptual | 0 | train |
496C8703EBA5C4C6BCD6D65EE60D3E768F1BF071 | https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/int-azure.html?context=cdpaas&locale=en | Integrating with Microsoft Azure | Integrating with Microsoft Azure
Integrating with Microsoft Azure You can configure an integration with the Microsoft Azure platform to allow IBM watsonx users access to data sources from Microsoft Azure. Before proceeding, make sure you have proper permissions. For example, you'll need permission in your subscription ... | how-to | 1 | train |
1C863B2624AB2712318442337C917143C19E7DDD | https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/create-job-pipelines.html?context=cdpaas&locale=en | Creating jobs for Pipelines | Creating jobs for Pipelines
Creating jobs for Pipelines You can create jobs for Pipelines. To create a Pipelines job: 1. Open your Pipelines asset from the project. 2. Click Run pipeline > Create a job. 3. On the Create a job page, you can choose the asset version that you'd like to run. The most recently saved version... | how-to | 1 | train |
28C4D682B46E9723F538988BB2BDB1EB65618E5E | https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/jobs.html?context=cdpaas&locale=en | Creating and managing jobs in a project | Creating and managing jobs in a project
Creating and managing jobs in a project You create jobs to run assets or files in tools, such as Data Refinery flows, SPSS Modeler flows, Notebooks, and scripts, in a project. When you create a job you define the properties for the job, such as the name, definition, environment r... | how-to | 1 | train |
F1CDB96AD5A56206F662BB3025B93F6D5820242B | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/plotnodeslots.html?context=cdpaas&locale=en | plotnode properties | plotnode properties
plotnode properties The Plot node shows the relationship between numeric fields. You can create a plot by using points (a scatterplot) or lines. plotnode properties Table 1.... | conceptual | 0 | train |
98AA3E34D14723232D266A85CBB9E2B1816B1AA5 | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-refine.html?context=cdpaas&locale=en | Quick start: Refine data | Quick start: Refine data
Quick start: Refine data You can save data preparation time by quickly transforming large amounts of raw data into consumable, high-quality information that is ready for analytics. Read about the Data Refinery tool, then watch a video and take a tutorial that’s suitable for beginners and does n... | how-to | 1 | train |
6F544922DE2638796837398F7EC15A4AFE6B0781 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/spss-algorithms.html?context=cdpaas&locale=en | SPSS predictive analytics algorithms | SPSS predictive analytics algorithms
SPSS predictive analytics algorithms You can use the following SPSS predictive analytics algorithms in your notebooks. Code samples are provided for Python notebooks. Notebooks must run in a Spark with Python environment runtime. To run the algorithms described in this section, you ... | conceptual | 0 | train |
2C0EBF0CCB497F41C14A5895EF97C01864BFC3D2 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/clem_reference/clem_function_ref_spatial.html?context=cdpaas&locale=en | Spatial functions (SPSS Modeler) | Spatial functions (SPSS Modeler)
Spatial functions Spatial functions can be used with geospatial data. For example, they allow you to calculate the distances between two points, the area of a polygon, and so on. There can also be situations that require a merge of multiple geospatial data sets that are based on a spati... | conceptual | 0 | train |
033F114BFF6D5479C2B4BE7C1542A4C778ABA53E | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/jython/clementine/python_add_attributes.html?context=cdpaas&locale=en | Adding attributes to a class instance | Adding attributes to a class instance
Adding attributes to a class instance Unlike in Java, in Python clients can add attributes to an instance of a class. Only the one instance is changed. For example, to add attributes to an instance x, set new values on that instance: x.attr1 = 1 x.attr2 = 2 . . x.attrN = n | how-to | 1 | train |
C2DA4BDE14D0A2DA1E0E2D795E7DC7469F422DB9 | https://dataplatform.cloud.ibm.com/docs/content/wsj/ai-risk-atlas/output-bias.html?context=cdpaas&locale=en | {{ document.title.text }} | {{ document.title.text }}
Output bias Risks associated with outputFairnessNew Description Generated model content might unfairly represent certain groups or individuals. For example, a large language mod... | conceptual | 0 | train |
C3552C5E0F334C8BC3557960821DC5EF931851A1 | https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/asset-activities.html?context=cdpaas&locale=en | Activities for assets | Activities for assets
Activities for assets For some asset types, you can see the activities of each asset in projects. The activities graph shows the history of the events that are performed on the asset for some tools. An event is an action that changes or copies the asset. For example, editing the asset description ... | conceptual | 0 | train |
A8A2D53661EB9EF173F7CC4794096A134123DACA | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-entity-enhanced.html?context=cdpaas&locale=en | Entity extraction | Entity extraction
Entity extraction The Watson Natural Language Processing Entity extraction models extract entities from input text. For details, on available extraction types, refer to these sections: * [Machine-learning-based extraction for general entities](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyz... | conceptual | 0 | train |
8ED36D5E1CCDFB0139D9D3DB3AEA2B90AE1B405E | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/svm.html?context=cdpaas&locale=en | SVM node (SPSS Modeler) | SVM node (SPSS Modeler)
SVM node The SVM node uses a support vector machine to classify data. SVM 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 Expert... | conceptual | 0 | train |
4D299EFFF5B982097A5B9D48EA16041E4820A8BB | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/derive.html?context=cdpaas&locale=en | Derive node (SPSS Modeler) | Derive node (SPSS Modeler)
Derive node One of the most powerful features in watsonx.ai is the ability to modify data values and derive new fields from existing data. During lengthy data mining projects, it is common to perform several derivations, such as extracting a customer ID from a string of Web log data or creati... | conceptual | 0 | train |
2904E26946523BB3E78975F68A822F5F2A32B9F5 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/clem_reference/clem_function_ref_trigonometric.html?context=cdpaas&locale=en | Trigonometric functions (SPSS Modeler) | Trigonometric functions (SPSS Modeler)
Trigonometric functions All of the functions in this section either take an angle as an argument or return one as a result. CLEM trigonometric functions Table 1. CLEM trigonometric functions Function Result Description arccos(NUM) Real Computes the arccosine of the specified angle... | conceptual | 0 | train |
6F35B89192B6C9A233B859CF66FCC435F3F9E650 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/kmeansnodeslots.html?context=cdpaas&locale=en | kmeansnode properties | kmeansnode properties
kmeansnode properties The K-Means node clusters the data set into distinct groups (or clusters). The method defines a fixed number of clusters, iteratively assigns re... | conceptual | 0 | train |
3AF15CB9E302A9E0D7DE22DE648EF7B3DCA1D865 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/autoai-ts-uni-tutorial.html?context=cdpaas&locale=en | Tutorial: AutoAI univariate time series experiment | Tutorial: AutoAI univariate time series experiment
Tutorial: AutoAI univariate time series experiment Use sample data to train a univariate (single prediction column) time series experiment that predicts minimum daily temperatures. When you set up the experiment, you load data that tracks daily minimum temperatures for... | how-to | 1 | train |
4CD539B8153216F80B26729A35AD4CD04A9C27DB | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fl-models.html?context=cdpaas&locale=en | Creating the initial model | Creating the initial model
Creating the initial model Parties can create and save the initial model before training by following a set of examples. * [Save the Tensorflow model](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fl-models.html?context=cdpaas&locale=entf-config) * [Save the Scikit-learn mo... | how-to | 1 | train |
B08A6B7A0F11FD3AB62A14F44FD4E1A771174C61 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/run-cuh-deploy-spaces.html?context=cdpaas&locale=en | Compute options for model training and scoring | Compute options for model training and scoring
Compute options for model training and scoring When you train or score a model or function, you choose the type, size, and power of the hardware configuration that matches your computing needs. * [Default hardware configurations](https://dataplatform.cloud.ibm.com/docs/con... | conceptual | 0 | train |
BACAF30043E33912E3D7F174B3F8CF858CB3093A | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/clem_reference/clem_function_ref_sequence.html?context=cdpaas&locale=en | Sequence functions (SPSS Modeler) | Sequence functions (SPSS Modeler)
Sequence functions For some operations, the sequence of events is important. The application allows you to work with the following record sequences: * Sequences and time series * Sequence functions * Record indexing * Averaging, summing, and comparing values * Monitoring change—differe... | conceptual | 0 | train |
C143A9F5185D9303301630D3FC53B604D3DCED2E | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_bandwidth_forecast_build.html?context=cdpaas&locale=en | Creating the flow (SPSS Modeler) | Creating the flow (SPSS Modeler)
Creating the flow 1. Add a Data Asset node that points to broadband_1.csv. 2. To simplify the model, use a Filter node to filter out the Market_6 to Market_85 fields and the MONTH_ and YEAR_ fields. Figure 1. Example flow to show Time Series modeling  | Importing a stream (SPSS Modeler)
Importing an SPSS Modeler stream You can import a stream ( .str) that was created in SPSS Modeler Subscription or SPSS Modeler client. 1. From your project's Assets tab, click . 2. Select Local file, select the .str file you want to import, and click Create. If the imported stream cont... | how-to | 1 | train |
D21CD926CA1FE170C8C1645CA0EC65AEDDDB4AEF | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/create-gist.html?context=cdpaas&locale=en | Publishing a notebook as a gist | Publishing a notebook as a gist
Publishing a notebook as a gist A gist is a simple way to share a notebook or parts of a notebook with other users. Unlike when you publish to a GitHub repository, you don't need to manage your gists; you can edit your gists directly in the browser. All project collaborators, who have ad... | how-to | 1 | train |
CB130D4E1AE505CE39CBD49BF9D22359B9EC80AB | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/cartnodeslots.html?context=cdpaas&locale=en | cartnode properties | 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 us... | conceptual | 0 | train |
6647035446FC3A28586EBABC619D10DB5FE3F4FD | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/merge.html?context=cdpaas&locale=en | Merge node (SPSS Modeler) | 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. ... | conceptual | 0 | train |
AF2AC67B66D3A2DB0D4F2AF2D6743F903F1385D7 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/install-cust-lib.html?context=cdpaas&locale=en | Installing custom libraries through notebooks | Installing custom libraries through notebooks
Installing custom libraries through notebooks The prefered way of installing additional Python libraries to use in a notebook is to customize the software configuration of the environment runtime associated with the notebook. You can add the conda or PyPi packages through a... | how-to | 1 | train |
9A83A33ABB4C6A12A7457D3711C2511EB3982B2C | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/clem_reference/clem_function_ref_string.html?context=cdpaas&locale=en | String functions (SPSS Modeler) | String functions (SPSS Modeler)
String functions With CLEM, you can run operations to compare strings, create strings, or access characters. In CLEM, a string is any sequence of characters between matching double quotation marks ("string quotes"). Characters (CHAR) can be any single alphanumeric character. They're decl... | conceptual | 0 | train |
422554C1DCEBABC93CB859B4A896908DA48A540D | https://dataplatform.cloud.ibm.com/docs/content/wsj/model/wos-setup-wos.html?context=cdpaas&locale=en | Setting up watsonx.governance | Setting up watsonx.governance
Setting up watsonx.governance You can set up watsonx.governance to monitor model assets in your IBM watsonx projects or deployment spaces. To set up watsonx.governance, you can manage users and roles for your organization to control access to your projects or deployment spaces. To set up w... | how-to | 1 | train |
3F3162BCD9976ED764717AA7004D9A755648B465 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/autoai-build.html?context=cdpaas&locale=en | Building an AutoAI model | Building an AutoAI model
Building an AutoAI model AutoAI automatically prepares data, applies algorithms, and builds model pipelines that are best suited for your data and use case. Learn how to generate the model pipelines that you can save as machine learning models. Follow these steps to upload data and have AutoAI ... | how-to | 1 | train |
ED7AFE85422B1DB8EAED166840D275DDDB63CAFA | https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/account-settings.html?context=cdpaas&locale=en | Managing your account settings | Managing your account settings
Managing your account settings From the Account window you can view information about your IBM Cloud account and set the Resource scope, Credentials for connections, and Regional project storage settings for IBM watsonx. * [View account information](https://dataplatform.cloud.ibm.com/docs... | how-to | 1 | train |
C4773EF8B0935E8DE084C1A6285EFE11E2A5F80A | https://dataplatform.cloud.ibm.com/docs/content/wsd/tutorials/tut_autoflag_models.html?context=cdpaas&locale=en | Generating and comparing models (SPSS Modeler) | Generating and comparing models (SPSS Modeler)
Generating and comparing models 1. Attach an Auto Classifier node, open its BUILD OPTIONS properties, and select Overall accuracy as the metric used to rank models. 2. Set the Number of models to use to 3. This means that the three best models will be built when you run th... | how-to | 1 | train |
A304B9E82543C150236ECAD30F1594E1B832B8B1 | https://dataplatform.cloud.ibm.com/docs/content/wsj/ai-risk-atlas/attribute-inference-attack.html?context=cdpaas&locale=en | {{ document.title.text }} | {{ document.title.text }}
Attribute inference attack Risks associated with inputInferencePrivacyAmplified Description An attribute inference attack is used to detect whether certain sensitive features can ... | conceptual | 0 | train |
A7845D8C3E419CEDD06E8C447ADF41E6E3D860C8 | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fed-lea.html?context=cdpaas&locale=en | IBM Federated Learning | IBM Federated Learning
IBM Federated Learning Federated Learning provides the tools for multiple remote parties to collaboratively train a single machine learning model without sharing data. Each party trains a local model with a private data set. Only the local model is sent to the aggregator to improve the quality of... | conceptual | 0 | train |
0F58073F0D5B237C3241126E98851A9E0C912792 | https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/ta_upload_tap-template_TMnode.html?context=cdpaas&locale=en | Uploading a text analysis package (TAP) in a Text Mining node (SPSS Modeler) | Uploading a text analysis package (TAP) in a Text Mining node (SPSS Modeler)
Uploading a custom asset in a Text Mining node You can add a custom text analysis package (TAP) or template directly in the Text Mining node. When your SPSS Modeler flow runs, it will use your custom asset. Procedure 1. If you want to download... | how-to | 1 | train |
E0E5646EA00A170BB595E9E0BBCCB69F702FFC7C | https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/data-science.html?context=cdpaas&locale=en | Analyzing data and working with models | Analyzing data and working with models
Analyzing data and working with models You can analyze data and build or work with models in projects. The methods that you choose for preparing data or working models help you determine which tools best fit your needs. Each tool has a specific, primary task. Some tools have capab... | conceptual | 0 | train |
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