400 Machine Learning Interview Questions with Answers 2026

Algorithms & Math: Linear/Logistic Regression, Tree-based models, Ensembles, and Loss Functions.

Practical ML Engineering: Feature engineering, Scikit-learn pipelines, and Hyperparameter tuning.

Advanced Topics: Deep Learning, Transformers, LLMs, RAG, and Vector Databases.

MLOps & Ethics: CI/CD for ML, Data Drift, Model Governance, and Fairness.

B) Increasing the threshold generally increases Precision but decreases Recall.

C) Decreasing the threshold increases Precision while keeping Recall constant.

D) The threshold has no impact on Precision if the dataset is perfectly balanced.

E) Increasing the threshold decreases Precision but increases Recall.

F) Precision and Recall are mathematically independent of the classification threshold.

Correct Answer: B

Overall Explanation: The classification threshold determines the cutoff for assigning a class. As you raise the threshold, the model becomes more "conservative," labeling only high-probability instances as positive, which usually reduces false positives (higher precision) but misses more actual positives (lower recall).

Detailed Option Analysis:

B) Correct: Higher thresholds lead to fewer positive predictions, reducing false positives (Precision up) but increasing false negatives (Recall down).

C) Incorrect: Decreasing the threshold typically increases Recall but lowers Precision.

D) Incorrect: Thresholding affects metrics regardless of class balance.

E) Incorrect: This is the opposite of the standard behavior.

F) Incorrect: Both metrics are derived from the Confusion Matrix, which changes based on the threshold.

Question 2: Which technique is specifically designed to address "High Variance" in a Random Forest model?

B) Decreasing the number of trees in the forest.

C) Increasing the minimum number of samples required to split an internal node.

D) Removing all regularization constraints from the base learners.

E) Using a learning rate of 1.0.

F) Switching from Bagging to a single Deep Decision Tree.

Correct Answer: C

Overall Explanation: High variance indicates overfitting. To combat this, you must constrain or "prune" the trees to prevent them from learning noise in the training data.

Detailed Option Analysis:

B) Incorrect: More trees generally reduce variance through averaging.

C) Correct: This acts as a regularization constraint, forcing trees to be simpler and more generalized.

D) Incorrect: Removing constraints increases the risk of overfitting.

E) Incorrect: Random Forests do not typically use a "learning rate" (that is specific to Boosting).

F) Incorrect: A single deep tree has significantly higher variance than a forest.

Question 3: When designing a RAG (Retrieval-Augmented Generation) system, what is the primary purpose of a Vector Database?

B) To store and retrieve documents based on their semantic embedding proximity.

C) To fine-tune the weights of the Large Language Model in real-time.

D) To act as a primary relational storage for user metadata and passwords.

E) To reduce the latency of token generation during the decoding phase.

F) To replace the LLM entirely by generating text from scratch.

Correct Answer: B

Overall Explanation: Vector databases store data as high-dimensional vectors (embeddings), allowing the system to find relevant context by calculating mathematical similarity rather than just keyword overlaps.

Detailed Option Analysis:

B) Correct: They enable semantic search by finding "nearest neighbors" in vector space.

C) Incorrect: RAG provides context; it does not change the model's internal weights.

D) Incorrect: Relational databases (SQL) are better suited for structured metadata.

E) Incorrect: While they assist retrieval speed, they don't change how the LLM decodes tokens.

F) Incorrect: Vector DBs provide data; the LLM is still required to synthesize that data into a response.

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