Supervised Learning

Q. Top 50 Supervised Learning Interview Questions with detailed Answers (All free)
Q. Top 25 Interview Questions on Classification with detailed Answers (All free)
Q. Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
Q. What is Supervised Learning?
Q. What is Classification?
Q. Regression vs. Classification
Q. What is Multi-class Classification?
Q. What is Bagging? How do you perform bagging and what are its advantages?
Q. Explain the concept and working of the Random Forest model
Q. What is a Decision Tree? Explain the concept and working of a Decision tree model
Q. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
Q. What is the difference between Discriminative and Generative models?
Q. What is Logistic Regression?
Q. Differentiate between Linear Models and Non Linear Models
Q. What does Gradient in Gradient Boosted Trees refer to?
Q. What are some of the common algorithms used for classification?
Q. What are the advantages and disadvantages of logistic regression?
Q. Distinguish between a Weak learner and a Strong Learner
Q. How would you address an imbalanced classification problem?
Q. What are some approaches for modeling non linear relationships?
Q. Explain the concept of Linear Regression
Q. What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
Q. What is a Generalized Linear Model (GLM)?
Q. How Does Naive Bayes Work?
Q. What is Regularization?
Q. What are some methods of Variable Selection?
Q. What are the assumptions of linear regression?
Q. What is CART?
Q. How are coefficients of linear regression estimated?
Q. What is the error / loss function in logistic regression?
Q. Explain the difference between Entropy, Gini, and Information Gain
Q. What does L1 regularization (Lasso) mean?
Q. What does L2 regularization (Ridge) mean?
Q. When to use Ridge Regression vs Lasso?
Q. What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Q. What are the options for reporting feature importance from a decision-tree based model?
Q. What are potential problems encountered in Linear Regression?
Q. What are the assumptions of logistic regression?
Q. How are the coefficients in a logistic expression interpreted?
Q. Briefly discuss other models that fall within the scope of GLM.
Q. How would you evaluate a classification model?
Q. How would you evaluate a Classification model using ROC/AUC?
Q. What is the difference between Decision Trees, Bagging and Random Forest?
Q. What is the kernel trick in SVM?
Q. What are common choices to use for kernels in SVM?
Q. How does hinge loss differ from logistic loss?
Q. Explain how SVM can be used in regression problems
Q. What are some of the pros/cons of SVM?
Q. How would you perform feature selection using Lasso?
Q. What are the advantages and disadvantages of a GBM model?
Q. How does a decision tree create splits from continuous features?
Q. How does pruning a tree work?
Q. What are the advantages and disadvantages of Decision Tree model?
Q. What are the key hyperparameters for a Random Forest model?
Q. What are the advantages and disadvantages of Random Forest?
Q. Why is Random Forest a non-linear model? Why does it result in non-linear decision boundaries?
Q. What are the key hyperparameters for a GBM model?
Q. How is Gradient Boosting different from Random Forest?
Q. GBM vs Random Forest: which algorithm should be used when?
Q. What are the best ways to safeguard against overfitting a GBM?
Q. What is the difference between Adaboost and Gradient boost?
Q. What is XGBoost? How does it improve upon standard GBM?
Q. How is variability measured in Linear Regression?
Q. What is multicollinearity and how can that be identified?
Q. What are the evaluation criteria for a Linear Regression model?
Q. What is Global F-Test?
Q. What is R-squared and adjusted R-squared?
Q. What are the various measures of error (MSE, RMSE, MAE)?
Q. What is Information Criteria (AIC, BIC)?
Q. What are some of the problems with stepwise selection approaches?
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. How can categorical predictors be incorporated in linear regression?
Q. What are the most common transformations when the target variable is not normally distributed?
Q. Doesn’t polynomial regression violate the multicollinearity assumption for Linear Regression?
Q. Why does multicollinearity result in poor estimates of coefficients in linear regression?
Q. What is the difference between Regression and ANOVA?
Q. What is the difference between outliers, high leverage points, and high influence points?
Q. What is an outlier?
Q. What is a high leverage point?
Q. What is a high influence point?
Q. What is non-negative least squares, and when is it used?
Q. What problems would arise from using a regular linear regression to model a binary outcome?
Q. Why are the log odds used in the link function instead of just the regular odds ratio?
Q. What is the relationship between the log odds ratio and probability?
Q. Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?
Q. What is the equivalent of the overall F test in logistic regression?
Q. How does GLM adjust to the case of count data?
Q. What is the cost function used in Poisson Regression?
Q. What is overdispersion in Poisson Regression, and what are alternate specifications for when it is present?
Q. What about cases where a significant number of observations have a count of 0 (in the context of Poisson Regression)?
Q. What is Gamma Regression?
Q. What is Beta regression?
Q. What is Tweedie Regression?
Q. What is Accuracy?
Q. What is Misclassification rate?
Q. What is Recall?
Q. What is Precision?
Q. What is F1 Score?
Q. What is Specificity?
Q. What is False Positive Rate (FPR)?

Partner Ad

Join us on:

Find out all the ways that you can

Contribute

Supervised Learning

Other Questions in Supervised Learning