Train/Test Split

Summary

Holding out a portion of your data to evaluate a model on examples it never trained on — the basic safeguard against fooling yourself with an overfit model.

How to Learn This

1Practice `train_test_split` and understand the effect of the split ratio.
2Learn stratified splitting for classification tasks with imbalanced classes.
3Understand why you must never evaluate on data the model has seen during training.

scikit-learn Basics

Cross-Validation

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised Learning Linear Regression Logistic Regression Decision Trees Random Forests K-Nearest Neighbors K-Means Clustering Support Vector Machines Naive Bayes scikit-learn Basics Cross-Validation

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