Decision Tree and Random Forest Modeling in Python

Decision trees are known in the machine learning world for a particularly distinctive characteristic: their visualizations are easier to understand compared to other machine learning models, and for this reason, they are very suitable for explaining insights to non-technical audiences.

In this course, you’ll learn the foundations of Decision Trees including identifying the key components of trees, interpreting them, classifying new observations using decision trees and calculating optimal thresholds for both classification and regression trees. You’ll also learn how to build and visualize decision trees by adapting a real-life dataset to train tree models, selecting the appropriate scikit-learn tools to build your model, and training, testing and visualizing decision trees.

You’ll be able to evaluate and optimize trees for better performance including activities such as establishing the optimal depth for a decision tree, using Prune decision trees to avoid overfitting, or manipulating sample distribution in nodes and leaves.

Finally, you’ll learn how to apply the cross validation and ensemble techniques for decision trees. You’ll identify the differences between decision trees and random forest models, develop and customize random forest models and optimize the parameters of random forest.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills in a project to predict employee productivity with tree models