Machine Learning Interview Questions for Freshers

Bias is error from oversimplifying assumptions (underfitting). Variance is error from sensitivity to small data fluctuations (overfitting). High bias: model too simple, misses patterns. High variance: model too complex, memorizes noise. The tradeoff: reducing bias typically increases variance. Optimal models minimize total error = bias² + variance + irreducible noise. In practice: regularization (L1/L2), dropout, and cross-validation help find the right balance.

Precision = TP/(TP+FP): of predicted positives, how many are correct? Recall = TP/(TP+FN): of actual positives, how many did we catch? F1 = harmonic mean of precision and recall. Choose based on cost: high precision needed when false positives are costly (spam detection — don't flag real emails). High recall needed when false negatives are costly (cancer screening — don't miss cases). F1 balances both.