Microsoft·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Microsoft machine learning interview, one question about overfitting that sounds deceptively simple until you actually have to commit to an answer out loud.

Questions Asked (1)

Among these four options, which is most likely to cause a model to overfit: adding more training data, reducing the number of nodes in hidden layers, removing sparse features, or using a Gaussian/RBF kernel in an SVM?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

The RBF kernel answer is correct since it can create very complex decision boundaries that hug the training data too closely, but I second-guessed myself halfway through.

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AI HintsAI Generated

Suggested Approach

Evaluate each option by considering its effect on model complexity and bias-variance trade-off. Identify which option increases model flexibility or capacity, leading to overfitting. Then confirm by explaining why the other options either reduce overfitting or have no effect.

Pro tip: Relate the answer to the bias-variance trade-off and mention that overfitting occurs when model complexity is high relative to the amount of training data and feature informativeness.

1. Define overfitting

Overfitting happens when a model learns noise in the training data, performing well on training but poorly on unseen data. It is often caused by excessive model complexity relative to the data.

2. Analyze each option's impact on model complexity

Adding more training data reduces overfitting; reducing hidden layer nodes reduces model capacity and overfitting; removing sparse features reduces dimensionality and noise, thus reducing overfitting; using a Gaussian/RBF kernel in SVM increases model flexibility and can lead to overfitting.

3. Identify the correct answer

The Gaussian/RBF kernel is most likely to cause overfitting because it can create highly complex decision boundaries, especially with high gamma values.

4. Explain why others are incorrect

Briefly state that the other options are techniques to mitigate overfitting or have no overfitting effect.

Key Points to Mention

Bias-variance trade-off: overfitting corresponds to high variance and low bias.
Model complexity: RBF kernel increases effective number of parameters, leading to high variance.
Regularization: SVM with RBF kernel requires careful tuning of C and gamma to avoid overfitting.
Data augmentation: more training data reduces variance.
Feature selection: removing sparse features can reduce noise and overfitting.
Network capacity: reducing hidden nodes decreases model complexity and overfitting.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.