Optimize Feature Selection and Handling in Machine Learning Models

Q: Optimize Feature Selection and Handling in Machine Learning Models

This question evaluates feature engineering competencies—scaling and normalization effects on different algorithms, strategies for handling missing or zero-inflated numeric predictors, and approaches to detect and remediate multicollinearity for feature selection in predictive models.

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Question

Scenario

You are building a customer propensity model to predict the probability that a user will take a desired action (e.g., purchase, subscribe). You have mixed feature types from transactions, web/app activity, and demographics.

Task

Answer the following practical feature-engineering questions for this setting.

Questions

When should we standardize or normalize variables? (Discuss the impact of scaling on different algorithms.)
How would you handle numeric predictors that contain many null or zero values? (Discuss imputation versus flagging and approaches for zero-inflated features.)
If several features are highly correlated, how would you decide which one(s) to keep? (Discuss multicollinearity detection and remedies.)

Optimize Feature Selection and Handling in Machine Learning Models

Scenario

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Optimize Feature Selection and Handling in Machine Learning Models

Overview

Scenario

Task

Questions

Solution

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