In a previous article, we learned how to build a random forest classifier. The regressor improved our tree model by training many tree models and selecting the best. Now, we will see how to do the same for regression. In this article, we will see how to create a Random Forest Regressor in Sklearn.
To create a RandomForestRegressor, we use the
RandomForestRegressor class from the
ensemble module. We create a instance of
RandomForestRegressor then pass our data to the
fit method as we usually do when building models.
from sklearn.ensemble import RandomForestRegressor from sklearn import datasets iris = datasets.load_iris() features = iris.data target = iris.target randomforest = RandomForestRegressor() model = randomforest.fit(features, target) print(model.score())