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Machine Learning Interview Questions

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?
Q. What are the advantages and disadvantages of Random Forest?
Q. What are the key hyperparameters for a Random Forest model?
Q. Explain the concept and working of the Random Forest model
Q. What is Bagging? How do you perform bagging and what are its advantages?
Q. What are the advantages and disadvantages of Decision Tree model?
Q. What is CART?
Q. How does pruning a tree work?
Q. Explain the concept of Linear Regression
Q. How does a decision tree create splits from continuous features?
Q. Explain the difference between Entropy, Gini, and Information Gain
Q. What is a Decision Tree? Explain the concept and working of a Decision tree model
Q. What is Clustering?
Q. Regression vs. Classification
Q. What is Classification?
Q. What is Unsupervised learning?
Q. What is Supervised Learning?

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Explore Questions by Topics

Computer Vision (1)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)
Deep Learning (52)
- DL Architectures (17)
  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Basics (16)
- DL Training and Optimization (17)
Generative AI (2)
Machine Learning Basics (18)
Natural Language Processing (27)
- NLP Data Preparation (18)
Statistics (34)
Supervised Learning (115)
- Classification (70)
  - Classification Evaluations (9)
  - Ensemble Learning (24)
  - Logistic Regression (10)
  - Other Classification Models (9)
  - Support Vector Machine (9)
- Regression (41)
  - Generalized Linear Models (9)
  - Linear Regression (26)
  - Regularization (6)
Unsupervised Learning (55)
- Clustering (37)
  - Clustering Evaluations (6)
  - Distance Measures (9)
  - Gaussian Mixture Models (5)
  - Hierarchical Clustering (3)
  - K-Means Clustering (9)
- Dimensionality Reduction (9)

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Other Questions in Machine Learning Interview Questions

How does SVM adjust for classes that cannot be linearly separated?
What is Bi-Clustering? What are possible use cases of it?
How to perform Standardization in case of outliers?
What is a Multilayer Perceptron (MLP) or a Feedforward Neural Network (FNN)?
Distinguish between a Weak learner and a Strong Learner
What are the pros and cons of parametric vs. non-parametric models?

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