🚀 Course: Scikit-Learn A to Z
🔁 Module 27: Cross Validation
🧠 Lecture B: Evaluation Design
👇 Link to the notebook:
🚀 Evaluation design in machine learning helps you avoid data leakage and get reliable model validation results with scikit-learn. This lecture explains how to choose train-test splits that respect row order, time structure, repeated entities, and grouped data, then shows when to use randomized shuffling, stratification, GroupKFold, nested cross-validation, and permutation significance testing. You’ll also learn how to interpret multi-metric evaluation, precision, recall, ROC AUC, fit time, score time, pipeline-wide validation, and fold score variation using logistic regression examples. Watch to build more trustworthy cross-validation workflows before tuning models.
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