Questions stringlengths 15 439 | Answers stringlengths 15 1.64k |
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Difference between SuperVised and Unsupervised Learning? | Supervised learning is when you know the outcome and you are provided with the fully labeled outcome data while in unsupervised you are not
provided with labeled outcome data. Fully labeled means that each example in the training dataset is tagged with the answer the algorithm should
come up with on its own. So, a labe... |
What is Reinforcment Learning and how would you define it? | A learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be
explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current
knowledge) .Semisupervised learning ... |
What is Deep Learning ? | Deep learning is defined as algorithms inspired by the structure and function of the brain called artificial neural networks(ANN).Deep learning
most probably focuses on Non Linear Analysis and is recommend for Non Linear problems regarding Artificial Intelligence. |
Difference between Machine Learning and Deep Learning? | Since DL is a subset of ML and both being subset of AI.While basic machine learning models do become progressively better at whatever their
function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make
adjustments. With a deep learning model, a... |
Difference between Bias and Variance? | Bias is definned as over simpliciation assumption assumed by the model,
Variance is definned as ability of a model to learn from Noise as well, making it highly variant.
There is always a tradeoff between these both, hence its recommended to find a balance between these two and always use cross validation to
determine ... |
What is Linear Regressions ? How does it work? | Fitting a Line in the respectable dataset when drawn to a plane, in a way that it actually defines the correlation between your dependent
variables and your independent variable. Using a simple Line/Slope Formulae. Famously, representing f(X) = M(x) + b.
Where b represents bias
X represent the input variable (independe... |
UseCases of Regressions: | Poisson regression for count data.
Logistic regression and probit regression for binary data.
Multinomial logistic regression and multinomial probit regression for categorical data.
Ordered logit and ordered probit regression for ordinal data. |
What is Logistic Regression? How does it work? | Logistic regression is a statistical technique used to predict probability of binary response based on one or more independent variables.
It means that, given a certain factors, logistic regression is used to predict an outcome which has two values such as 0 or 1, pass or fail,
yes or no etc
Logistic Regression is used... |
What is Logit Function? or Sigmoid function/ where in ML and DL you can use it? | The sigmoid might be useful if you want to transform a real valued variable into something that represents a probability. While the Logit function
is to map probaliticvalues from -Inf to +inf to either real numbers representing True or False towards 1 or 0 (real number). This is commonly used
in Classification having b... |
What is Support Vector Machine ? how is it different from OVR classifiers? | Support Vector Machine is defineed as a Technique which is classification and regression model. Which uses hyper plan estimation and best hyper plane
fitting the estimate on linear lines drawn same a linear one. Although it can also work for non Linear using kernal tricks on SVM.
SVM is totally based on Marginal lines ... |
Types of SVM kernels | Think of kernels as definned filters each for their own specific usecases.
1) Polynomial Kernels (used for image processing)
2) Gaussian Kernel (When there is no prior knowledge for data)
3) Gaussian Radial Basis Function(same as 2)
4) Laplace RBF Kernel ( recommend for higher training set more than million)
5) Hyperbo... |
What is different type of Evauation metrics in Regression? | There are multiple evaluation metrics for Regression Analysis
1) Mean Squared Error ( the average squared difference between the estimated values and the actual value)
2) Mean Absolute Error (Absolute of the Average difference)
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How would you define Mean absolute error vs Mean squared error? | MAE : Use MAE when you are doing regression and don’t want outliers to play a big role. It can also be useful if you know that your distribution is multimodal, and it’s desirable to have predictions at one of the modes, rather than at the mean of them.
MSE : use MSE the other way around, when you want to punish the out... |
How would you evaluate your classifier? | A classifier can be evaluated through multiple case, having the base case around its confusion metrics and its attributes which are TP, TN , FP and FN. Along with the Accuracy metrics which can be derived alongside Precision, Recall scores. |
What is Classification? | Classification is defined as categorizing classes or entities based on the specified categories either that category exists or not in the respectable data. The concept is quite common for Image based classification or Data Based Classification. The answer in form of Yes or No;
alongside answers in form of types of obje... |
How would you differentiate between Multilabel and MultiClass classification? | A multiclass defines as a classification outcome which can be of multiple classes either A or B or C but not two or more than one.
While in MultiLabel classification, An outcome can be of either one or more than two classes i.e A or A and B or A and B and C.
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What is Confusion Matrics? | A confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of
the performance of an algorithm, typically a supervised learning one (in unsupervised learning it is
usually called a matching matrix). Each row of the matrix represents the instances in a predicted class
while ea... |
Which Algorithms are High Biased Algorithms? | Bias is the simplifying assumptions made by the model to make the target function easier to approximate.
1) High bias algorithms are most probably Linear Algorithm, which are concerned with linear relationships or linear distancing. Examples are
2) Linear, Logistic or Linear Discrimenant Analysis. |
Which Algorithms are High and low Variance Algorithms? | Variance is the amount that the estimate of the target function will change given different training data
1) High Variance Algorithms are Decision Trees, K Nearest Neigbours and SVMs
2) Low Variance Algorithms are Linear Regression, Logistic Regression and LDA' |
Why are the above algorithms are High biased or high variance? | Linear machine learning algorithms often have a high bias but a low variance.
Nonlinear machine learning algorithms often have a low bias but a high variance. |
What are root case of Prediction Bias? | Possible root causes of prediction bias are:
1) Incomplete feature set
2) Noisy data set
3) Buggy pipeline
4) Biased training sample
5) Overly strong regulariz |
What is Gradient Decent? Difference between SGD and GD? | Gradient Descent is an iterative method to solve the optimization problem. There is no concept of "epoch" or "batch" in classical gradient decent. The key of gradient decent are
* Update the weights by the gradient direction.
* The gradient is calculated precisely from all the data points.
Stochastic Gradient Descent c... |
What is Randomforest and Decision Trees? | A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event
outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
Random forests or random decision forests are an ens... |
What is Process of Splitting? | Splitting up your data in to subsets based on provided data facts. (can come in handy for decision Trees) |
What is the process prunning? | The Shortening of branches of Decision Trees is termed as Prunning. The process is done in case to reach the decision quite earlier than
expected. Reducing the size of the tree by turning some branch nodes into leaf nodes, and removing the leaf nodes under the original branch. |
How do you do Tree Selection? | Tree selection is mainly done from the following
1) Entropy
A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar
values (homogeneous). ID 3 algorithm uses entropy to calculate the homogeneity of a sample. If the sample is completely homog... |
Pseudocode for Entropy in Decision Trees: | '''
from math import log
def calculateEntropy(dataSet):
number = len(dataSet)
labelCounts = {}
for featureVector in dataSet:
currentLabel = featureVector[-1]
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel] = 0
labelCounts[currentLabel] +=1
entropy = 0
for i in labelCounts:
probability = float(label... |
How does RandomForest Works and Decision Trees? | -* Decision Tree *- A Simple Tree compromising of the process defined in selection of Trees.
-* RandomForest *- Combination of Multiple N number of Decision Trees and using the aggregation to determine the final outcome.
The classifier outcome is based on Voting of each tree within random forest while in case of regres... |
What is Gini Index? Explain the concept? | The Gini Index is calculated by subtracting the sum of the squared probabilities of each class from one. It favors larger partitions.
Imagine, you want to draw a decision tree and wants to decide which feature/column you should use for your first split?, this is probably defined
by your gini index. |
What is the process of gini index calculation? | Gini Index:
for each branch in split:
Calculate percent branch represents .Used for weighting
for each class in branch:
Calculate probability of class in the given branch.
Square the class probability.
Sum the squared class probabilities.
Subtract the sum from 1. #This is the Ginin Index for branch
Weight each branch b... |
What is the formulation of Gini Split / Gini Index? | Favors larger partitions.
Uses squared proportion of classes.
Perfectly classified, Gini Index would be zero.
Evenly distributed would be 1 – (1/# Classes).
You want a variable split that has a low Gini Index.
The algorithm works as 1 – ( P(class1)^2 + P(class2)^2 + … + P(classN)^2) |
What is probability? How would you define Likelihood? | Probability defines the percentage of Succes occured. or Success of an event. Can be described Chance of having an event is 70% or etc.
We suppose that the event that we get the face of coin in success, so the probability of success now is 0.5 because the probability of face and back of a coin is equal. 0.5 is the prob... |
What is Entropy? and Information Gain ? there difference ? | Entropy: Randomness of information being processed.
Information Gain multiplies the probability of the class times the log (base=2) of that class probability. Information Gain favors smaller partitions with many distinct values. Ultimately, you have to experiment with your data and the splitting criterion.
IG depends... |
What is KL divergence, how would you define its usecase in ML? | Kullback-Leibler divergence calculates a score that measures the divergence of one probability distribution from another
|
How would you define Cross Entropy, What is the main purpose of it ? | Entropy: Randomness of information being processed.
Cross Entropy: A measure from the field of information theory, building upon entropy and generally calculating the difference between two
probability distributions. It is closely related to but is different from KL divergence that calculates the relative entropy betwe... |
How would you define AUC - ROC Curve? | ROC is a probability curve and AUC represents degree or measure of separability. AUC - ROC curve is a performance measurement for classification problem at various thresholds settings.
It tells how much model is capable of distinguishing between classes. Mainly used in classification problems for measure at different t... |
How would you define False positive or Type I error and False Negative or Type II Error ? | False positive : A false positive is an outcome where the model incorrectly predicts the positive class.(was A but got predicted B) aka Type I error.
False Negative : A false negative is an outcome where the model incorrectly predicts the negative class. (was not A but predicted A) aka Type II error. |
How would you define precision() and Recall(True positive Rate)? | Take a simple Classification example of "Classifying email messages as spam or not spam"
Precision measures the percentage of emails flagged as spam that were correctly classified—that is, the percentage of dots to the right of the threshold line, it is also defined as % of event being Called at positive rates e.g
Prec... |
Which one would you prefer for you classification model Precision or Recall? | This totally depends on Business Usecase or SME usecase. In case of Fraud Detection Business domains such as banks, online ecommerce websites
recommends of better recall score than precision. While in other cases such as word suggestions or Multi label Categorization it can be precision.
In general, totally dependent o... |
What is F1 Score? which intution does it gives ? | The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall).
Also known as Dice Similarity Coefficent.
David (Scientist Statistician): The widespread use of the F1 score since it gives equal importance to precision and recall. In practice, ... |
What is difference between Preceptron and SVM? | The major practical difference between a (kernel) perceptron and SVM is that perceptrons can be trained online (i.e. their weights can be updated
as new examples arrive one at a time) whereas SVMs cannot be. Perceptron is no more than hinge loss (loss function) + stochastic gradient descent (optimization).
SVM has almo... |
What is the difference between Logsitic and Liner Regressions? | LogR is Classifier, LR is Regression.
LogR values are between 0 and 1 and probabilty in between as well.
LR values are in realnumbers from 1 to postive N (where N is known)
|
What are outliers and How would you remove them? | An outlier is an observation that lies an abnormal distance from other values in a random sample from a population.
Outliers can be removed by following:
1) Use Inter Quantile Range (IQR * 1.5)
2) Use Z-score Scale removal (so that any point much away from mean gets removed)
3) Combination of Z Score and IQR (custom s... |
What is Regulization? | Regularizations are techniques used to reduce the error by fitting a function appropriately on the given training set and avoid overfitting.
Add Lambda * Biasness value at the end. |
Difference between L1 and L2 Regulization? | 1) L1 Regulization (Lasso Regression)
(Least Absolute Shrinkage and Selection Operator) adds “absolute value of magnitude” of coefficient as penalty term to the loss function.
2) L2 Regulization (Ridge Regression)
(adds “squared magnitude” of coefficient as penalty term to the loss function. Here the highlighted part r... |
What are different Technique of Sampling your data? | Data Sampling statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the larger data set being examined.
There are different techniques of sampling your data
1) Simple Random Sampling (records are picked at random)
2) Stratified Sa... |
Can you define the concept of Undersampling and Oversampling? | Undersampling is the concept of downsizing the class based sample from a Bigger range to smaller range i.e 1Million records to 0.1 Million records,
keeping the ratio of information intact
Oversampling represents the concept of using a smaller class sample i.e 100K to scale upto million keeping the trend and the propert... |
What is Imbalanced Class? | Imbalancment is when you don't have balance in between classes.
Imabalnced class is when the normal distribution/support count of multiple classes or classes being considered are not the same or almost same.
E.G:
Class A has 1 Million Record
Class B has 1000 Record
This is imbalanced data set and Class B is UnderBalanc... |
How would you resolve the issue of Imbalancment data set? | The techniques such as
1) OverSampling
2) UnderSampling
3) Smote combination of both
4) bringing in more dataset
5) doing more trend analysis |
How would you define Weighted Moving Averages ? | A incremental increase of Moving Average having a Weighted Multiple to keep the values which gets repeated during a certain time with High
priority/ Impact. |
What is meant by ARIMA Models? | A Regressive and Moving Average Model combination is termed as ARIMA. To be exact, Auto Regressive Intergerated Moving Avearges.
A techniques which does regression analysis along with moving averages which fits time series analysis and gets trend analysis with
acceptable scores. |
How would you define Bagging and Boosting? How would XGBoost differ from RandomForest? | Bagging : A way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data.
Example : Random Forest (uses random Sampling subsets)
Boosting: An iterative technique which adjusts the weight of an obser... |
What is IQR, how can these help in Outliers removal? | IQR is interquantile range which specifies the range between your third quantile and the first one.
Quantile are 4 points of your data represented by percentage(should be four equal parts )
Q1: 0-25%
Q2: 25-50%
Q3: 50-75%
Q4: 75- 100%
IQR :- Q3 - Q1 |
What is SMOTE? | Synthetic Minority Over-sampling TEchnique also known as SMOTE.
A very popular oversampling method that was proposed to improve random oversampling but
its behavior on high-dimensional data has not been thoroughly investigated.
KNN algorithm gets benefits from SMOTE. |
How would you resolve Overfitting or Underfitting? | Underfitting
1) Increase complexity of model
2) Increasing training time
3) decrease learning rate
Overfitting:
1) Cross Validation
2) Early Stops
3) increased learning rates(hops)
4) Ensembling
5) Bring in More data
6) Remove Fea |
Mention some techniques which are to avoid Overfitting? | 1) Cross Validation
2) Early Stops
3) increased learning rates(hops)
4) Ensembling
5) Bring in More data
6) Remove F |
What is a Neuron? | A "neuron" in an artificial neural network is a mathematical approximation of a biological neuron.
It takes a vector of inputs, performs a transformation on them, and outputs a single scalar value.
It can be thought of as a filter. Typically we use nonlinear filters in neural networks. |
What are Hidden Layers and Input layer? |
1) Input Layer: Initial input for your neural network
2) Hiddent layers: a hidden layer is located between the input and output of the algorithm,
in which the function applies weights to the inputs and directs them through an activation function as the output.
In short, the hidden layers perform nonlinear transformati... |
What are Output Layers? | Output layer in ANN determines the final layer which is responsible for the final outcome, the outcome totally depends
on the usecase provided and the function which is being used to scale the values. By default, Linear, Sigmoid and Relu are
most common choices.
Linear for Regression.
Sigmoid/Softmax for Classification... |
What are activation functions ? | Activation functions perform a transformation on the input received, in order to keep values within a manageable range depending
on the limitation of the activation function. Its more of a mathematical scale filter applied to a complete layer (Vector)
to scale out values.
Some common examples for AF are:
1) Sigmoid or ... |
What is Convolotional Neural Network? | convolutional-neural-network is a subclass of neural-networks which have at least one convolution layer.
They are great for capturing local information (e.g. neighbor pixels in an image or surrounding words in a text)
as well as reducing the complexity of the model (faster training, needs fewer samples, reduces the cha... |
What is recurrent Neural Network? | A class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence.
This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their
internal state (memory) to process variable length sequences of inputs. This makes them... |
What is LSTM network? | Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture.
Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single
data points (such as images), but also entire sequences of data (such as speech or video).
For example, LSTM is applicabl... |
What is Convolotional Layer? | A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the
same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a
detected feature in an input, such as an image.
You can use Filters which ar... |
What is Pooling Layer? | Pooling layers provide an approach to down sampling feature maps by summarizing the presence of features in patches of the feature map.
Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated
presence of a feature respectively.
This is requi... |
What is MaxPooling Layer? How does it work? | Max polling uses the Maximum value found in a considered size metrics. Maximum pooling, or max pooling, is a pooling operation that calculates the maximum, or largest, value in each patch of each feature map.in a way.
|
What is Kernel or Filter? | kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM)
Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors.
A Kernel is used to solve Non- Linear problem by Linear Classifiers in a way that its useable.
|
What is Segmentation? | The process of partitioning a digital source into multiple segments.
If you refer Image, Imagine Image source being converted into multiple segments such as Airplane object.
The goal of segmentation is to simplify and/or change the representation of an image into something that
is more meaningful and easier to analyze. |
What is Pose Estimation? | Detection of poster from an Image is represented as Post Estimation. |
What is Forward propagation? | The input data is fed in the forward direction through the network. Each hidden layer accepts the input data,
processes it as per the activation function and passes to the successive layer.
|
What is backward propagation? | Back-propagation is the essence of neural net training. It is the practice of fine-tuning the weights
of a neural net based on the error rate (i.e. loss) obtained in the previous epoch (i.e. iteration).
Proper tuning of the weights ensures lower error rates, making the model reliable by increasing its generalization
|
what are dropout neurons? | The term “dropout” refers to dropping out units (both hidden and visible) in a neural network.
Simply put, dropout refers to ignoring units (i.e. neurons) during the training phase of certain
set of neurons which is chosen at random. By “ignoring”, I mean these units are not considered during
a particular forward or ba... |
what are flattening layers? | A flatten layer collapses the spatial dimensions of the input into the channel dimension.
For example, if the input to the layer is an H-by-W-by-C-by-N-by-S array (sequences of images),
then the flattened output is an (H*W*C)-by-N-by-S array. |
How is backward propagation dealing an improvment in the model? | practice of fine-tuning the weights of a neural net based on the error rate (i.e. loss)
obtained in the previous epoch (i.e. iteration). Proper tuning of the weights ensures lower error rates,
making the model reliable by increasing its generaliz |
What is correlation? and covariance? | “Covariance” indicates the direction of the linear relationship between variables.
“Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables.
When comparing data samples from different populations, covariance is used to determine how much two random variab... |
What is Anova? when to use Anova? | Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures
(such as the "variation" among and between groups) used to analyze the differences among group means in a sample.
Use a one-way ANOVA when you have collected data about one categorical independent variable and
... |
How would you define dimentionality reduction? why we use dimentionality reduction? | Dimensionality reduction or dimension reduction is the process of reducing the number of random variables
under consideration by obtaining a set of principal variables. Approaches can be divided into feature
selection and feature extraction.
The reason we use it is because
1) Immensive dataset
2) Longer Trainnig time/g... |
What is Principle componenet analysis? how does PCA work in Dimentonality reduction? | The main linear technique for dimensionality reduction, principal component analysis, performs
a linear mapping of the data to a lower-dimensional space in such a way that the variance of
the data in the low-dimensional representation is maximized. In practice, the covariance (and
sometimes the correlation) matrix of t... |
What is Maximum Likelihood estimation? | Maximum likelihood estimation is a method that determines values for the parameters of a model.
The parameter values are found such that they maximise the likelihood that the process described by the model
produced the data that were actually observed. |
What is Naive Bayes? How does it works? | A method of estimating the parameters of a probability distribution by maximizing a likelihood function,
so that under the assumed statistical model the observed data is most probable
|
What is Bayes Theorm? | The probability of an event, based on prior knowledge of conditions that might be related to the event. |
What is Probability? | Probability is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility and 1 indicates certainty.
The higher the probability of an event, the more likely it is that the event will occur.
Example:
A simple example is the tossing of a fair (unbiased)
coin. Since the coin is fair, the two outcomes ("h... |
What is Joint Probability? | Joint probability is a statistical measure that calculates the likelihood of two events occurring together and at the same point in time.
P(A and B) or P (A ^ B) or P(A & B)
The joint probability is detremeinded as :
P(A and B) = P(A given B) * P(B)
|
What is Marginal Probability? | Probability of event X=A given variable Y. Single Random event probability
P(A) , A single probability of an independent event. |
What is Conditional Probability? what is distributive Probability? | Probability of event A given event B is termed as Conditional Probability. |
What is Z score? | Z score (also called the standard score) represents the number of standard deviations with which the
value of an observation point or data differ than the mean value of what is observed |
What is KNN how does it works? what is neigbouring criteria? How you can change it ? | KNN is dependent on distancing estimation from the points of a Class to respectable points in class, thus acting as a Vote Based Neigbouring
Classifier, where you conclude the outcome of your input to be predicted by measuring which points come close to it.
You can have as much as neigbours you want, the more you speci... |
Which one would you prefer low FN or FP's based on Fraudial Transaction? | Recommended is low FN's, the reason is because if you consider Fraudly Transaction being occured and counting it as not being occured
This has huge impact on the Business model. |
Differentiate between KNN and KMean? | KMean: Unsupervised, Random points drawn, each uses distance based averages for prediction.
KNN: Supervised, neigbouring, C values , Voting
|
What is Attention ? Give Example ? | A neural attention mechanism equips a neural network with the ability to focus on a subset of its inputs (or features).
1) Hard Attention (Image Cropping)
2) Soft Attention (Highlight attentional area keeping the image size same)
|
What are AutoEncoders? and what are transformers? | Autoencoders take input data, compress it into a code, then try to recreate the input data from that summarized code. It’s like starting with Moby Dick, creating a SparkNotes version and then trying to rewrite the original story using only SparkNotes for reference. While a neat deep learning trick, there are fewer real... |
What is Image Captioning? | Image Captioning is the process of generating textual description of an image. It uses both Natural Language Processing and Computer Vision to
generate the captions
|
Give some example of Text summarization. | Summarization is the task of condensing a piece of text to a shorter version, reducing the size of the initial text while preserving the meaning.
Some examples are :
1) Essay Summarization
2) Document Summarization
etc |
Define Image Segmentation and Pose Analysis? | Image Segmentation : In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into
multiple segments (sets of pixels, also known as image objects). The goal of segmentation is to simplify and/or change the representation of
an image into something that is more m... |
Define Semantic Segmentation? | Semantic Segmentation is the Segmentation of an image based on Type of Objects
|
What is Instance Segmentation? | Same as Semantic, although with Objects (with respectable ID's) |
Define Text Classification, Give some usecase examples? | Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups.
By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined
tags or categories based on its content.
UseCases:
1) Document... |
which algorithms to use for Missing Data? |
REFERENCED FROM : https://github.com/andrewekhalel/MLQ |
1) What's the trade-off between bias and variance? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)] | If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. [[src]](https://t... |
2) What is gradient descent? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)] | [[Answer]](https://machinelearningmastery.com/gradient-descent-for-machine-learning/)
Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost).
Gradient descent is best used when the parameters cannot be calculated analyti... |
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