First round: Questions on my background. What is Linear Regression (LR), and why do we use it? What are LS assumptions? How do you address overfitting? What are the different methods of Regularization? Why is Collinearity not desired?

Second Round: Questions on my background. Why do more features lead to overfitting? What are other methods of Regularization? What are hyperparameters in Random Forest, XGBoost, and FF NN? What is the optimization formula of SVM? What are transformation methods in NLP, and explain the pros and cons of each of them? How do we evaluate a model? What are the pros and cons of cross-validation? What optimization algorithm do you suggest for a sparse matrix in classification?

Question

First round: Questions on my background. What is Linear Regression (LR), and why do we use it? What are LS assumptions? How do you address overfitting? What are the different methods of Regularization? Why is Collinearity not desired?

Second Round: Questions on my background. Why do more features lead to overfitting? What are other methods of Regularization? What are hyperparameters in Random Forest, XGBoost, and FF NN? What is the optimization formula of SVM? What are transformation methods in NLP, and explain the pros and cons of each of them? How do we evaluate a model? What are the pros and cons of cross-validation? What optimization algorithm do you suggest for a sparse matrix in classification?

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