Explain core ML fundamentals

Q: Explain core ML fundamentals

This is a Machine Learning interview question from Amazon for Software Engineer roles. View the full question and solution on PracHub.

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Question

Machine Learning Fundamentals: Regularization, Losses, PCA, and Random Forests

Assume standard supervised learning with linear models for regression/classification, PCA for dimensionality reduction, and Random Forests for tabular data. Answer the following:

1) L1 vs. L2 Regularization

Compare L1 (Lasso) and L2 (Ridge) regularization in terms of:

Sparsity of learned coefficients
Optimization geometry and differentiability
Robustness to outliers (clarify what kind of outliers and how the penalty interacts with the loss)

2) Choosing Loss Functions and Gradient Properties

Explain how to choose loss functions for:

Regression: MSE vs. MAE (and mention Huber if relevant)
Classification: logistic/cross-entropy (and note hinge/focal if relevant) Discuss their gradient properties, optimization behavior, and sensitivity to outliers.

3) PCA

Describe PCA’s objective (variance maximization vs. reconstruction error minimization), the fitting and transform steps, and how to select the number of components.

4) Random Forests

Explain how Random Forests are trained, their bias–variance trade-off, the limits of impurity-based feature importance, and key hyperparameters (with brief tuning guidance).