Preparing for senior Machine Learning and Data Science roles requires mastering fundamental mathematical trade-offs, regularization, and model validation techniques. Here are 15 essential interview questions with architect-level answers.

1. Explain the Bias-Variance Trade-off in Machine Learning.

Bias error represents the simplifying assumptions made by a model to render the target function easier to approximate. High bias leads to underfitting. Variance error represents the sensitivity of the model to fluctuations in the training dataset. High variance leads to overfitting.

Total Error = Bias^2 + Variance + Irreducible Error