Top 25 Interview Questions With Answers For Machine Learning

Prepare to ace your next machine learning interview with these top 25 questions and detailed answers that will set you apart from other candidates.

Top 25 Machine Learning Interview Questions with Answers

1. What is Machine Learning and how is it different from traditional programming?

Machine learning (ML) is a subset of artificial intelligence where machines learn patterns from data without being explicitly programmed. Unlike traditional programming, where logic is manually coded, ML enables systems to learn and improve from experience.

2. What are the different types of Machine Learning?

• Supervised Learning: Learns from labeled data.
• Unsupervised Learning: Learns from unlabeled data to find hidden patterns.
• Semi-supervised Learning: Mix of labeled and unlabeled data.
• Reinforcement Learning: Learns through rewards and penalties by interacting with the environment.

3. Explain Overfitting and Underfitting.

• Overfitting: Model performs well on training data but poorly on unseen data due to excessive learning of noise.
• Underfitting: Model fails to capture underlying patterns, resulting in poor performance on both training and test data.

4. What is the difference between Classification and Regression?

Classification is about predicting discrete labels or categories (e.g., spam or not). Regression is about predicting continuous values (e.g., house price).

5. What is the Bias-Variance Tradeoff?

• Bias: Error from wrong assumptions in the learning algorithm.
• Variance: Error from sensitivity to small fluctuations in training data.
• A good model finds a balance between low bias and low variance.

6. What is the Curse of Dimensionality?

It refers to the problem where increasing the number of features makes the data sparse, degrading the model's performance due to difficulty in calculating distances and relationships.

7. What are some common performance metrics for classification models?

• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC Score
• Confusion Matrix

8. Explain Precision and Recall.

Precision is the ratio of correctly predicted positives to total predicted positives. Recall is the ratio of correctly predicted positives to all actual positives. High precision means fewer false positives, while high recall means fewer false negatives.

9. What is Cross-Validation and why is it used?

Cross-validation splits the dataset into training and testing subsets multiple times to ensure that the model performs well across different data portions and avoids overfitting.

10. What is the difference between Bagging and Boosting?

• Bagging: Reduces variance by training models in parallel and aggregating results (e.g., Random Forest).
• Boosting: Reduces bias by training models sequentially, focusing on errors of previous models (e.g., XGBoost).

11. What is a Confusion Matrix?

12. What is Feature Engineering and why is it important?

Feature engineering involves creating new features or transforming existing ones to improve model accuracy. It helps models learn better patterns and can significantly boost performance.

13. What is PCA (Principal Component Analysis)?

PCA is a dimensionality reduction technique that transforms features into a set of linearly uncorrelated components, preserving as much variance as possible.

14. How do you handle missing data in a dataset?

• Remove rows/columns with missing values.
• Impute using mean, median, or mode.
• Use algorithms that support missing values.
• Predict missing values using other features.

15. What are Hyperparameters and how do you tune them?

Hyperparameters are configuration settings set before training (e.g., learning rate, depth). They're tuned using techniques like Grid Search, Random Search, or Bayesian Optimization to find optimal values for best performance.

16. What is the purpose of Regularization?

17. Explain the difference between L1 and L2 regularization.

• L1 (Lasso): Adds absolute value of coefficients, leads to sparse models.
• L2 (Ridge): Adds square of coefficients, reduces impact without eliminating features.
• L1 can be used for feature selection.

18. What is an ROC curve?

The ROC (Receiver Operating Characteristic) curve plots the True Positive Rate vs. False Positive Rate. AUC (Area Under Curve) closer to 1 indicates a good classifier.

19. What is Gradient Descent?

Gradient Descent is an optimization algorithm that minimizes loss by iteratively updating parameters in the direction of the steepest descent (negative gradient).

20. What is the difference between Batch, Stochastic, and Mini-Batch Gradient Descent?

• Batch: Uses the entire dataset.
• Stochastic: Uses one sample per iteration.
• Mini-Batch: Uses small batches, balancing speed and accuracy.

21. What is the role of the learning rate in training?

The learning rate controls how much weights are adjusted during training. Too high can overshoot minima; too low can lead to slow convergence.

22. How is Naive Bayes used in Machine Learning?

Naive Bayes is a probabilistic classifier based on Bayes’ Theorem with strong independence assumptions. It is effective for text classification problems like spam detection.

23. What is the difference between parametric and non-parametric models?

• Parametric: Assumes a fixed number of parameters (e.g., Linear Regression).
• Non-parametric: No fixed parameter assumption, more flexible (e.g., KNN, Decision Trees).

24. What is K-means Clustering?

25. What is Model Drift and how do you handle it?

Model drift happens when the statistical properties of input data change over time, degrading model performance. It is addressed by monitoring models and retraining with recent data.