Compare Logistic Regression and Random Forest in Limited Data Scenarios
Overview
This question evaluates a data scientist's understanding of binary classification model selection, covering logistic regression fundamentals, L1/L2 regularization effects, overfitting detection, and comparisons between Random Forest and boosting within the Machine Learning domain.
Google
Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Machine Learning
68
0
Model Selection for Binary Classification with Limited Data and Potential Non-Linearities
Scenario
You are designing a binary classifier with limited labeled data. The signal may be partly non-linear, and you care about generalization and interpretability.
Questions
- What is logistic regression, and what is its loss function? Briefly note its optimization properties (convexity).
- When can logistic regression outperform a Random Forest?
- Explain L1 and L2 regularization and their effects (e.g., sparsity, multicollinearity).
- How would you detect and mitigate overfitting in logistic regression?
- Compare Random Forest and Boosting (e.g., Gradient Boosting) in terms of bias, variance, interpretability, and typical use cases. Include thoughts on ensemble diversity and probability calibration.
Solution
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