The default value for this parameter is 2, which means that an internal node must have at least two samples before it can be split to have a more specific classification.Ĥ. min_samples_split: The min_samples_split parameter specifies the minimum number of samples required to split an internal leaf node. A pure leaf is one where all of the data on the leaf comes from the same class.ģ.
The default value for max_depth is None, which means that each tree will expand until every leaf is pure. max_depth: The max_depth parameter specifies the maximum depth of each tree. The default value for this parameter is 10, which means that 10 different decision trees will be constructed in the random forest.Ģ. n_estimators: The n_estimators parameter specifies the number of trees in the forest of the model.In this post, I will be investigating the following four parameters: For a Random Forest Classifier, there are several different hyperparameters that can be adjusted. Different models have different hyperparameters that can be set. Most generally, a hyperparameter is a parameter of the model that is set prior to the start of the learning process. In order to optimize this model to create the most accurate predictions, I will be focusing solely on hyperparameter adjustment and selection. The purpose of this classification model is to determine whether a wine is red or white. For the purpose of this post, I have combined the individual datasets for red and white wine, and assigned both an extra column to distinguish the color of the wine, where 0 represents a red wine and 1 represents a white wine. I will be analyzing the wine quality datasets from the UCI Machine Learning Repository. In this post, I will be taking an in-depth look at hyperparameter tuning for Random Forest Classification models using several of scikit-learn’s packages for classification and model selection.
CARET RANDOM FOREST HOW TO
What hyperparameters are, how to choose hyperparameter values, and whether or not they’re worth your time
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