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Machine Learning Concepts Q&A
1What is Machine Learning?
Answer: Learning patterns from data to make predictions/decisions without explicit rules.
2Supervised vs unsupervised learning?
Answer: Supervised uses labeled data; unsupervised finds structure in unlabeled data.
3Classification vs regression?
Answer: Classification predicts categories; regression predicts continuous values.
4What is overfitting?
Answer: Model memorizes training noise and fails to generalize.
5What is underfitting?
Answer: Model is too simple to learn meaningful patterns.
6Bias-variance tradeoff?
Answer: Balance model simplicity and flexibility for best generalization.
7What is regularization?
Answer: Penalty terms that reduce overfitting by constraining complexity.
8Why feature scaling?
Answer: Helps distance/gradient-based models converge and behave consistently.
9What is cross-validation?
Answer: Repeated train/validation splits for robust performance estimation.
10What are confusion matrix metrics?
Answer: Precision, recall, F1, and specificity from TP/FP/TN/FN counts.
11What is ROC-AUC?
Answer: Threshold-independent measure of classification ranking quality.
12One-line ML concept summary?
Answer: ML is about building models that generalize reliably to unseen data.