Compare Logistic Regression and Random Forest in Limited Data Scenarios

Q: Compare Logistic Regression and Random Forest in Limited Data Scenarios

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.

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Question

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.

Compare Logistic Regression and Random Forest in Limited Data Scenarios

Model Selection for Binary Classification with Limited Data and Potential Non-Linearities

Scenario

Questions

Solution

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