Machine Learning Interview Questions and Answers

Supervised learning uses labelled examples, learning a mapping from inputs to known outputs. Tasks include classification (predict a category, such as spam or not) and regression (predict a number, such as price). Algorithms include linear and logistic regression, decision trees, gradient boosting and neural networks.

Unsupervised learning works with unlabelled data, finding structure. Tasks include clustering (grouping similar customers with k-means), dimensionality reduction (PCA, t-SNE) and anomaly detection. There is no ground-truth label to score against.

A third setting, self-supervised learning, generates labels from the data itself and underpins modern language and vision models. Reinforcement learning is another paradigm where an agent learns from reward signals.

Choose supervised when you have reliable labels and a clear target; choose unsupervised for exploration and segmentation when labels are unavailable or expensive.

Overfitting is when a model learns noise and idiosyncrasies of the training set instead of the underlying pattern. It performs well on training data but poorly on new data. Signs include a large gap between training and validation metrics and unstable performance across folds.

Prevention:

• More data and data augmentation.
• Simpler models or fewer features.
• Regularization: L1 or L2 for linear models, weight decay for neural networks.
• Early stopping based on validation loss.
• Cross-validation to detect instability.
• Dropout and batch normalization for deep networks.
• Pruning and depth or leaf limits for trees.
• Ensembling and bagging to reduce variance.

The counterpart is underfitting, where the model is too simple and misses signal. Balance the two using the bias-variance tradeoff and always keep a held-out test set for the final check.

Choose metrics that match the problem.

• MAE: mean absolute error, in the target's units, robust to outliers.
• MSE and RMSE: penalize large errors more; RMSE is in target units and sensitive to outliers.
• R-squared: fraction of variance explained, but it always rises with more features, so use adjusted R-squared.
• MAPE: percentage error, useful across scales but undefined when the actual is zero.

from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score

Always compare against a baseline, such as predicting the mean or the previous value. Inspect residual plots for patterns, heteroscedasticity and non-linearity, and check errors across segments, not just overall. Use cross-validation for stable estimates and a held-out test set for the final report.