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:

classifier = RandomForestClassifier(n_estimators = 100)

We now have a very powerful machine learning model that is fairly resistant to overfitting!



Create a RandomForestClassifier named classifier. When you create it, pass two parameters to the constructor:

  • n_estimators should be 2000. Our forest will be pretty big!
  • random_state should be 0. There’s an element of randomness when creating random forests thanks to bagging. Setting the random_state to 0 will 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?

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