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Selecting the best model in scikit-learn using cross-validation


In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting features. We'll compare cross-validation with the train/test split procedure, and we'll also discuss some variations of cross-validation that can result in more accurate estimates of model performance.

This is the seventh video in the series, Introduction to machine learning with scikit-learn. The notebook and resources shown in the video are available on GitHub.

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