How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at TikTok.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at TikTok during technical interviews.

Choose linear regression or decision tree appropriately

Quick Overview

This question evaluates model selection and diagnostic skills in supervised learning, specifically assessing feature engineering, interaction detection, handling heteroskedastic residuals, incorporation of monotonicity or interaction constraints in tree-based models, and fair cross-validation-based comparison between linear and tree approaches.

Choose Between Linear Regression and a Decision Tree Under a Hinge and Interaction DGP

Context

You have 100,000 i.i.d. observations with features x1 (range 0–100), x2, x3, and target y. The true data-generating process (unknown to you) is piecewise linear with a hinge at x1 = 50 and an interaction between x2 and x3:

y ≈ 3·x1 + 20·I[x1 > 50] + 2·(x2·x3) + ε
Heteroskedastic noise: Var(ε | x1) = 0.01·(1 + x1)

Task

Design an analysis to decide between linear regression and a decision tree. Specify:

Feature engineering and tests you would run for linearity (e.g., spline basis for x1, x2:x3 interaction) and how you would check residual diagnostics for heteroskedasticity.
A fair comparison protocol (CV split, identical preprocessing) and metrics.
How you would enforce monotonicity or interaction constraints in a tree-based model to reflect domain knowledge.
Which model you expect to generalize better here and why, including bias–variance reasoning and how you would quantify it with learning curves.

Quick Overview

Context

y ≈ 3·x1 + 20·I[x1 > 50] + 2·(x2·x3) + ε

Heteroskedastic noise: Var(ε | x1) = 0.01·(1 + x1)

Task

Design an analysis to decide between linear regression and a decision tree. Specify:

Feature engineering and tests you would run for linearity (e.g., spline basis for x1, x2:x3 interaction) and how you would check residual diagnostics for heteroskedasticity.

A fair comparison protocol (CV split, identical preprocessing) and metrics.

How you would enforce monotonicity or interaction constraints in a tree-based model to reflect domain knowledge.

Which model you expect to generalize better here and why, including bias–variance reasoning and how you would quantify it with learning curves.

Choose linear regression or decision tree appropriately

Quick Overview

Choose Between Linear Regression and a Decision Tree Under a Hinge and Interaction DGP

Context

Task

Solution

Comments (0)

Choose linear regression or decision tree appropriately

Quick Overview

Choose Between Linear Regression and a Decision Tree Under a Hinge and Interaction DGP

Context

Task

Solution

Comments (0)