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How to find the best model parameters in scikit-learn


In this video, you'll learn how to efficiently search for the optimal tuning parameters (or "hyperparameters") for your machine learning model in order to maximize its performance. I'll start by demonstrating an exhaustive "grid search" process using scikit-learn's GridSearchCV class, and then I'll compare it with RandomizedSearchCV, which can often achieve similar results in far less time.

This is the eighth 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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