You now have the ability to make a random forest using your own decision trees. However, scikit-learn has a RandomForestClassifier class that will do all of this work for you! RandomForestClassifier is in the sklearn.ensemble module.
RandomForestClassifier works almost identically to DecisionTreeClassifier — the .fit(), .predict(), and .score() methods work in the exact same way.
When creating a RandomForestClassifier, you can choose how many trees to include in the random forest by using the n_estimators parameter like this:
We now have a very powerful machine learning model that is fairly resistant to overfitting!
Instructions
Create a RandomForestClassifier named classifier. When you create it, pass two parameters to the constructor:
n_estimatorsshould be2000. Our forest will be pretty big!random_stateshould be0. There’s an element of randomness when creating random forests thanks to bagging. Setting therandom_stateto0will help us test your code.
Train the forest using the training data by calling the .fit() method. .fit() takes two parameters — training_points and training_labels.
Test the random forest on the testing set and print the results. How accurate was the model?