Summary

We have finally reached the end of this chapter on multiclass classification with Random Forest. We learned that multiclass classification is an extension of binary classification: instead of predicting only two classes, target variables can have many more values. We saw how we can train a Random Forest model in just a few lines of code and assess its performance by calculating the accuracy score for the training and testing sets. Finally, we learned how to tune some of its most important hyperparameters: n_estimators, max_depth, min_samples_leaf, and max_features. We also saw how their values can have a significant impact on the predictive power of a model but also on its ability to generalize to unseen data.

In real projects, it is extremely important to choose a valid testing set. This is your final proxy before putting a model into production so you really want it to reflect the types of data you think it will receive in the future. For instance, if your dataset has...