Supervised Learning

Q. Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
Q. Top 25 Interview Questions on Classification with detailed Answers (All free)
Q. Top 50 Supervised Learning Interview Questions with detailed Answers (All free)
Q. What is Logistic Regression?
Q. What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Q. How would you perform feature selection using Lasso?
Q. When to use Ridge Regression vs Lasso?
Q. What does L2 regularization (Ridge) mean?
Q. What does L1 regularization (Lasso) mean?
Q. What is Regularization?
Q. What is the difference between Discriminative and Generative models?
Q. What are some pros and cons of Discriminant Analysis?
Q. What is the difference between QDA and Gaussian Mixture Models (GMM)?
Q. What differentiates Linear Discriminant Analysis (LDA) from Quadratic Discriminant Analysis (QDA)?
Q. How does discriminant analysis work at a high level?
Q. What are some of the pros/cons of SVM?
Q. Explain how SVM can be used in regression problems
Q. How does hinge loss differ from logistic loss?
Q. Describe the hinge loss function used in SVM
Q. What hyper-parameters are typically tuned in SVM?
Q. What are common choices to use for kernels in SVM?
Q. What is the kernel trick in SVM?
Q. How does SVM adjust for classes that cannot be linearly separated?
Q. What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
Q. What are the Pros/Cons of Naive Bayes?
Q. How are continuous features incorporated into Naive Bayes?
Q. What happens if a category has a zero frequency within a class, and how is this issue commonly addressed (Naive Bayes)?
Q. How Does Naive Bayes Work?
Q. What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
Q. What do you mean by calibration quality? How can calibration quality be detected from the output of an algorithm?
Q. Understanding Probability Outputs in Classification Algorithms
Q. What are some of the common algorithms used for classification?
Q. What is Multi-class Classification?
Q. How to determine threshold/decision rule for a classification model?
Q. How would you address an imbalanced classification problem?
Q. How would you evaluate a Classification model using ROC/AUC?
Q. What is False Positive Rate (FPR)?
Q. What is Specificity?
Q. What is F1 Score?
Q. What is Precision?
Q. What is Recall?
Q. What is Misclassification rate?
Q. What is Accuracy?
Q. How would you evaluate a classification model?
Q. What is Tweedie Regression?
Q. What is Beta regression?
Q. What is Gamma Regression?
Q. Briefly discuss other models that fall within the scope of GLM.
Q. What about cases where a significant number of observations have a count of 0 (in the context of Poisson Regression)?
Q. What is overdispersion in Poisson Regression, and what are alternate specifications for when it is present?
Q. What is the cost function used in Poisson Regression?
Q. How does GLM adjust to the case of count data?
Q. What is the error / loss function in logistic regression?
Q. What are the advantages and disadvantages of logistic regression?
Q. What is the equivalent of the overall F test in logistic regression?
Q. Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?
Q. How are the coefficients in a logistic expression interpreted?
Q. What is the relationship between the log odds ratio and probability?
Q. Why are the log odds used in the link function instead of just the regular odds ratio?
Q. What problems would arise from using a regular linear regression to model a binary outcome?
Q. What are the assumptions of logistic regression?
Q. What is a Generalized Linear Model (GLM)?
Q. What is non-negative least squares, and when is it used?
Q. What are potential problems encountered in Linear Regression?
Q. What is a high influence point?
Q. What is a high leverage point?
Q. What is an outlier?
Q. What is the difference between outliers, high leverage points, and high influence points?
Q. What is the difference between Regression and ANOVA?
Q. Why does multicollinearity result in poor estimates of coefficients in linear regression?
Q. Doesn’t polynomial regression violate the multicollinearity assumption for Linear Regression?
Q. What are some approaches for modeling non linear relationships?
Q. Differentiate between Linear Models and Non Linear Models
Q. What are the most common transformations when the target variable is not normally distributed?
Q. How can categorical predictors be incorporated in linear regression?
Q. Suppose there are a large number of predictors ‘p’. What is the best approach to find out if any of the p predictors are helpful in predicting the response ‘y’?
Q. What are some of the problems with stepwise selection approaches?
Q. What is Information Criteria (AIC, BIC)?
Q. What are the various measures of error (MSE, RMSE, MAE)?
Q. What is R-squared and adjusted R-squared?
Q. What is Global F-Test?
Q. What are the evaluation criteria for a Linear Regression model?
Q. What is multicollinearity and how can that be identified?
Q. How is variability measured in Linear Regression?
Q. How are coefficients of linear regression estimated?
Q. What are some methods of Variable Selection?
Q. What are the assumptions of linear regression?
Q. What does Gradient in Gradient Boosted Trees refer to?
Q. What is XGBoost? How does it improve upon standard GBM?
Q. What is the difference between Adaboost and Gradient boost?
Q. Distinguish between a Weak learner and a Strong Learner
Q. What are the options for reporting feature importance from a decision-tree based model?
Q. What are the best ways to safeguard against overfitting a GBM?
Q. GBM vs Random Forest: which algorithm should be used when?
Q. How is Gradient Boosting different from Random Forest?
Q. What are the advantages and disadvantages of a GBM model?
Q. What are the key hyperparameters for a GBM model?
Q. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
Q. What is the difference between Decision Trees, Bagging and Random Forest?
Q. Why is Random Forest a non-linear model? Why does it result in non-linear decision boundaries?

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