Introduction to Data Science (DATA 101) Model Evaluation: Train/Test Split, Cross-Validation, and Metrics Practice Problems
Georgia Institute of Technology
Introduction to Data Science (DATA 101) | Practice Problems

Overview
A high-intent evaluation packet for DATA 101: how to split data correctly, validate models with cross-validation, and interpret common classification and regression metrics.

Key topics
•train/validation/test splits and data leakage
•cross-validation (k-fold) and when to use it
•confusion matrix terms (TP/FP/TN/FN)
•precision/recall/F1 and ROC-AUC intuition
•regression metrics (MAE, MSE, RMSE, R^2)

Suggested study tasks
•compute precision/recall/F1 from 6 confusion matrices
•choose a metric for 10 scenarios (imbalanced vs balanced)
•identify leakage in 8 mini-pipelines
•interpret 6 cross-validation summaries
•compute MAE/MSE/RMSE for 6 small prediction tables

Common pitfalls
•tuning on the test set
•using accuracy on imbalanced data
•mixing up precision vs recall
•reporting R^2 without residual checks
•forgetting stratification for classification splits