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Top 50 Supervised Learning Interview Questions with detailed Answers (All free)

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Supervised Learning Interview Questions

  1. What is supervised learning? What are some common algorithms used in supervised learning
  2. What is the difference between classification and regression in supervised learning?
  3. Explain the concept of feature selection in supervised learning
  4. What is Feature Standardization and why is it needed?
  5. What is overfitting, and how can it be prevented in supervised learning?
  6. What is underfitting and how can it be prevented?
  7. What does L1 regularization (Lasso) mean?
  8. What does L2 regularization (Ridge) mean?

Regression:

  1. What is Regression? What are some common regression algorithms?
  2. What are the assumptions in a Linear Regression model?
  3. How are coefficients of linear regression estimated?
  4. What are the key evaluation criteria for Linear Regression model?
  5. What are potential problems encountered in Linear Regression?

Classification:

  1. What is classification, and discuss the different types of classification?
  2. What are some common classification algorithms?
  3. How do you evaluate the performance of a classification model?
  4. What is a ROC curve?
  5. How do you handle imbalanced datasets in classification tasks?
  6. Explain the difference between Gini, Entropy, and Information Gain

Logistic Regression

  1. What is Logistic Regression?
  2. Describe the whole process of how to use logistic regression to fit data
  3. What are the major assumptions of logistic regression?
  4. What are the advantages and disadvantages of logistic regression?

Suppor Vector Machine (SVM)

  1. What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
  2. What hyper-parameters are typically tuned in SVM?
  3. What are the pros/cons of using an SVM model?
  4. What are common choices to use for kernels in SVM?
  5. Describe the hinge loss function used in SVM
  6. What is the kernel trick in SVM?

Ensemble Learning

  1.  What is a Decision Tree? Explain the concept and working of a Decision tree model
  2.  What is Bagging? How do you perform bagging and what are its advantages?
  3.  Explain the concept and working of the Random Forest model
  4.  What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
  5.  What is XGBoost? How does it improve upon standard GBM?
  6.  How is Gradient Boosting different from Random Forest?
  7.  What is the difference between Adaboost and Gradient boost?
  8.  Distinguish between a Weak learner and a Strong Learner
  9.  What parameters can be tweaked for a Random Forest model? Explain in detail 
  10.  GBM vs Random Forest: which algorithm should be used when?
  11.  What is the difference between Decision Trees, Bagging and Random Forest?
  12.  What are the advantages and disadvantages of Decision Tree model? 
  13.  What are the advantages and disadvantages of Random Forest?
  14.  What are the advantages and disadvantages of a GBM model?
  15.  How does pruning a tree work?

Other key questions

  1. What is a Generalized Linear Model (GLM)?
  2.  Briefly discuss other models that fall within the scope of GLM.
  3. What is the difference between a generative and a discriminative model?
  4. What is a naive bayes classifier? Explain how does Naive Bayes work
  5. What are the Pros/Cons of Naive Bayes? 
  6. How does discriminant analysis work at a high level?

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