Machine Learning Interview Questions - ML Interview Prep

Machine Learning is subset of AI that enables systems to learn and improve from experience without being explicitly programmed. Uses algorithms to identify patterns in data, make predictions, or decisions. Three types: supervised, unsupervised, reinforcement learning.

Supervised learning uses labeled data (input-output pairs) to train models. Unsupervised learning finds patterns in unlabeled data. Supervised: classification, regression. Unsupervised: clustering, dimensionality reduction, association rules.

Linear regression models relationship between dependent variable and one or more independent variables using linear equation: y = mx + b. Finds best-fit line minimizing sum of squared errors. Used for continuous predictions. Assumes linear relationship.

Logistic regression is classification algorithm that predicts probability using sigmoid function. Outputs values between 0 and 1. Uses log-odds (logit). Binary classification: predicts class based on probability threshold (usually 0.5). Can be extended to multi-class.

Decision tree makes decisions by splitting data based on feature values. Tree structure: root, internal nodes (decisions), leaves (outcomes). Uses information gain or Gini impurity for splits. Easy to interpret, prone to overfitting. Basis for random forests.

Random forest is ensemble method combining multiple decision trees. Each tree trained on random subset of data and features. Predictions averaged (regression) or voted (classification). Reduces overfitting, handles non-linearity, feature importance available.

Overfitting occurs when model learns training data too well, including noise, performs poorly on new data. Prevent with: more training data, cross-validation, regularization (L1/L2), early stopping, dropout, feature selection, ensemble methods, reducing model complexity.

Cross-validation splits data into k folds, trains on k-1 folds, tests on remaining fold, repeats k times. Provides better estimate of model performance than single train/test split. Common: k-fold (k=5 or 10), stratified k-fold, leave-one-out, time series CV.

Precision = TP / (TP + FP) - accuracy of positive predictions. Recall = TP / (TP + FN) - ability to find all positives. High precision: few false positives. High recall: few false negatives. F1-score balances both: 2 * (precision * recall) / (precision + recall).

ROC curve plots True Positive Rate vs False Positive Rate at different classification thresholds. AUC (Area Under Curve) measures classifier performance: 1.0 perfect, 0.5 random, >0.7 good. Higher AUC = better discrimination. Useful for binary classification evaluation.