The Decision Tree Algorithm in Python Video

The Decision Tree Algorithm in Python Video

Objectives:

1. Learn all about decision trees and their advantages and disadvantages.
2. Perform decision tree regression. Decision tree regression observes features of an object and trains a model to predict data to produce meaningful continuous output.
3. Understand the difference between decision tree regression and linear regression.
4. Explore the decision tree classifier. The data set is split into subsets based on an attribute value test, and subsets are continued to be created in a process called recursive partitioning.
5. Understand the difference between decision tree classification and linear regression.
6. Understand information gain in depth. Information gain is a measure of how much information a feature in a given dataset gives with respect to class.
7. Learn all about entropy, which plays an essential role in deciding how a decision tree will split data.
8. Create a decision tree using Python.
9. Create sample input for the model, use this sample input to have the model make a prediction, and then compare the precision to the actual output.
10. Evaluate a decision tree model using a confusion matrix.

Instructor: Dhiraj Kumar

Length: 1 hour

Access period: For one year starting from purchase date