The Decision Tree Algorithm in Python Video
- Learn all about decision trees and their advantages and disadvantages.
- Perform decision tree regression. Decision tree regression observes features of an object and trains a model to predict data to produce meaningful continuous output.
- Understand the difference between decision tree regression and linear regression.
- 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.
- Understand the difference between decision tree classification and linear regression.
- 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.
- Learn all about entropy, which plays an essential role in deciding how a decision tree will split data.
- Create a decision tree using Python.
- 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.
- Evaluate a decision tree model using a confusion matrix.
Instructor: Dhiraj Kumar
Length: 1 hour
Access period: For one year starting from purchase date