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Generate AI-powered Data Scientist interview questions instantly — technical, behavioral, and situational. Calibrated for experienced-hire interviews at Indian tech companies.
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A preview from our curated question bank. The generator produces fresh, AI-tailored questions on each run.
What is the difference between classification and regression?
Tip: Classification predicts a discrete category (spam/not spam). Regression predicts a continuous value (house price). Logistic regression is classification despite the name — a common exam trap.
Explain the bias-variance trade-off. How does it guide model selection?
Tip: Bias: error from wrong assumptions (underfitting — model too simple). Variance: error from sensitivity to training data (overfitting — model too complex). Goal: sweet spot that generalises. Regularisation trades some variance for lower bias.
What is cross-validation? Why is it better than a simple train-test split?
Tip: k-Fold CV splits data into k folds, trains k times each using a different fold as validation. Averages performance across folds for a more reliable estimate than a single split. Especially important for small datasets.
What is the difference between L1 (Lasso) and L2 (Ridge) regularisation?
Tip: L1 (sum of absolute weights): produces sparse models by driving some weights to exactly 0 — acts as feature selection. L2 (sum of squared weights): shrinks all weights towards 0 but rarely to exactly 0. Use L1 for feature selection, L2 for general regularisation.
What is the confusion matrix? Define precision, recall, and F1 score.
Tip: Precision = TP/(TP+FP) — of all predicted positives, how many are correct. Recall = TP/(TP+FN) — of all actual positives, how many did we catch. F1 = harmonic mean. High-precision when false positives are costly; high-recall when false negatives are costly.
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