AI and machine learning interview questions

More data, simpler models, regularization, dropout, early stopping, and cross-validation help.

Gradients shrink through deep layers so early weights barely learn.

Never shuffle future into training.

Use feature contributions, examples, and limits — not equations.

When data is tiny, interpretability is required, or a linear baseline already wins.

Fit preprocessors on train folds only and keep future info out of features.