DataCamp

A Random Forest analysis in Python

A detailed study of Random Forests would take this tutorial a bit too far. However, since it's an often used machine learning technique, gaining a general understanding in Python won't hurt.

In layman's terms, the Random Forest technique handles the overfitting problem you faced with decision trees. It grows multiple (very deep) classification trees using the training set. At the time of prediction, each tree is used to come up with a prediction and every outcome is counted as a vote. For example, if you have trained 3 trees with 2 saying a passenger in the test set will survive and 1 says he will not, the passenger will be classified as a survivor. This approach of overtraining trees, but having the majority's vote count as the actual classification decision, avoids overfitting.

Building a random forest in Python looks almost the same as building a decision tree; so we can jump right to it. There are two key differences, however. Firstly, a different class is used. And second, a new argument is necessary. Also, we need to import the necessary library from scikit-learn.

Use RandomForestClassifier() class instead of the DecisionTreeClassifier() class.
n_estimators needs to be set when using the RandomForestClassifier() class. This argument allows you to set the number of trees you wish to plant and average over.

The latest training and testing data are preloaded for you.

Build the random forest with n_estimators set to 100.
Fit your random forest model with inputs features_forest and target.
Compute the classifier predictions on the selected test set features.

Take Hint (-30xp)

Exercise

A Random Forest analysis in Python

Instructions