from sklearn.ensemble import RandomForestRegressor clf = RandomForestRegressor(max_depth=2, random_state=0) clf.fit(X, y) print(clf.predict([[0, 0, 0, 0]]))
Here is what the above code is Doing:
1. Creating a RandomForestRegressor object with max_depth=2 and random_state=0.
2. Fitting the object to the training data.
3. Predicting the labels of the test data.
The output is an array of predictions.
You can also use the RandomForestRegressor object to compute the feature importances.
The feature importances are the relative importance of each feature for making predictions.
The more important the feature, the more the model relies on it.
You can access the feature importances using the feature_importances_ property: