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 hard difficulty Machine Learning question, commonly asked during Onsite rounds at Instacart.

What role is this question designed for?

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

Contrast Lasso vs Ridge trade‑offs | Instacart Interview Question

Quick Overview

This question evaluates a Data Scientist's understanding of regularization methods (L1/Lasso, L2/Ridge, Elastic Net), their bias–variance trade-offs, variable selection behavior under multicollinearity, and the implications for inference on a treatment indicator in linear models.

Regularization choices for modeling contribution per order (p=50)

Context: You are building a linear model for contribution per order (continuous outcome) with about p = 50 covariates that include:

Highly correlated marketing dummy variables (e.g., overlapping campaigns, channels)
Weather variables
Daypart indicators

Assume predictors are standardized and that a binary treatment indicator D (e.g., exposed vs. not exposed to a marketing action) is of substantive interest for inference.

Tasks

Lasso vs. Ridge

Explain the bias–variance trade‑offs of L1 (Lasso) and L2 (Ridge).
Contrast their variable selection behavior under correlated groups of predictors.
Discuss how each affects uncertainty quantification for treatment effects, including best practices to avoid bias in the estimated treatment coefficient.

Elastic Net and tuning for valid inference

Describe when Elastic Net strictly dominates using Lasso or Ridge alone in this setting.
Explain how you would tune α and λ via cross‑validation, and how to keep inference valid after model selection (e.g., post‑selection refitting, stability selection).

Interactions and heterogeneity

Discuss how regularization interacts with collinearity when you include treatment×covariate interactions (D×X), and the risk of shrinking true heterogeneous effects to zero.

Quick Overview

Regularization choices for modeling contribution per order (p=50)

Context: You are building a linear model for contribution per order (continuous outcome) with about p = 50 covariates that include:

Highly correlated marketing dummy variables (e.g., overlapping campaigns, channels)

Weather variables

Daypart indicators

Assume predictors are standardized and that a binary treatment indicator D (e.g., exposed vs. not exposed to a marketing action) is of substantive interest for inference.

Tasks

Lasso vs. Ridge

Explain the bias–variance trade‑offs of L1 (Lasso) and L2 (Ridge).

Contrast their variable selection behavior under correlated groups of predictors.

Discuss how each affects uncertainty quantification for treatment effects, including best practices to avoid bias in the estimated treatment coefficient.

Elastic Net and tuning for valid inference

Describe when Elastic Net strictly dominates using Lasso or Ridge alone in this setting.

Explain how you would tune α and λ via cross‑validation, and how to keep inference valid after model selection (e.g., post‑selection refitting, stability selection).

Interactions and heterogeneity

Discuss how regularization interacts with collinearity when you include treatment×covariate interactions (D×X), and the risk of shrinking true heterogeneous effects to zero.

Contrast Lasso vs Ridge trade‑offs

Quick Overview

Regularization choices for modeling contribution per order (p=50)

Tasks

Solution

Comments (0)

Contrast Lasso vs Ridge trade‑offs

Quick Overview

Regularization choices for modeling contribution per order (p=50)

Tasks

Solution

Comments (0)