September 2018
Beginner to intermediate
154 pages
3h 51m
English
The method used to choose the best estimators for a particular dataset or choosing the best values for all hyperparameters is called hyperparameter tuning. Hyperparameters are parameters that are not directly learned within estimators. Their value is decided by the modelers.
For example, in the RandomForestClassifier object, there are a lot of hyperparameters, such as n_estimators, max_depth, max_features, and min_samples_split. Modelers decide the values for these hyperparameters.
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