Q1. What is the bias-variance tradeoff?
Bias is error from overly simple assumptions; a high-bias model underfits and misses patterns in both training and test data. Variance is error from sensitivity to the tr...
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Q2. What is the difference between supervised and unsupervised learning?
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 re...
Q3. How do you handle class imbalance in a classification problem?
Class imbalance means one class is far rarer, so accuracy becomes misleading and models favor the majority. Strategies: - Resampling: oversample the minority (SMOTE synth...
Q4. What is overfitting and how do you prevent it?
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 dat...
Q5. Explain precision and recall and when to prefer each.
Precision is the fraction of predicted positives that are truly positive: TP / (TP + FP). Recall, or sensitivity, is the fraction of actual positives that were found: TP ...
Q6. What is cross-validation and why use it?
Cross-validation repeatedly splits data into training and validation folds to estimate how well a model generalizes. In k-fold, the data is split into k parts; the model ...
Q7. What is regularization and how do L1 and L2 differ?
Regularization adds a penalty on model complexity to the loss, discouraging large weights and reducing overfitting. L2 (ridge) adds the sum of squared weights. It shrinks...
Q8. Explain how gradient descent optimizes a model.
Gradient descent minimizes a loss function by repeatedly moving parameters in the direction that decreases the loss. The gradient is the vector of partial derivatives, an...
Q9. How do you evaluate a regression model?
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 targe...
Q10. Compare random forests and gradient boosting.
Both are tree ensembles, differing in how trees are combined. Random forest builds many deep, independent trees on bootstrap samples with random feature subsets, then ave...