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.

Download the notebook: https://github.com/justmarkham/scikit-learn-videos
Grid search user guide: http://scikit-learn.org/stable/modules/grid_search.html
GridSearchCV documentation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
RandomizedSearchCV documentation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
Comparing randomized search and grid search: http://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html
Randomized search video: https://youtu.be/0wUF_Ov8b0A?t=17m38s
Randomized search notebook: https://github.com/amueller/pydata-nyc-advanced-sklearn/blob/master/Chapter%203%20-%20Randomized%20Hyper%20Parameter%20Search.ipynb
Random Search for Hyper-Parameter Optimization: http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf

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