Machine Learning Best Practices

Apply rigorous machine learning engineering standards across exploratory analysis, feature engineering, model training, and statistical performance validation.

Core Principle

Every model finding must be grounded in leakage-free splits, dual-model baselines, and statistical confidence intervals.

Core Invariants

1. Strict Train-Test Isolation: Always partition datasets into training, validation, and test splits before fitting any scalers, encoders, or transformers.
2. Mandatory Missing Value Strategy: Explicitly quantify missing value frequencies and document domain rationale for keeping, dropping, or imputing them.
3. Dual-Model Benchmark: Never rely on a single model architecture in isolation. Train at least two distinct candidate families against a naive baseline.
4. Statistical Significance Over Raw Averages: Report 95% bootstrap confidence intervals for key evaluation metrics (F1, AUC, RMSE) to verify statistical separation.
5. Story-First Notebook Structure: Every code execution cell must be paired with an analytical markdown cell explaining the observed patterns, trade-offs, and conclusions.