Machine learning in Python with scikit-learn - 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. 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 WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN "Data School Insiders" to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/