Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Results Prediction and Accuracy

The video tutorial explains how to create, train, and evaluate a decision tree classifier using Python. It begins with an introduction to the decision tree classifier and proceeds to demonstrate the creation of a classifier object using entropy as the criterion. The tutorial then covers training the classifier with data and concludes with predicting responses for a test dataset and evaluating the model's accuracy, which is found to be 75%.

Describe the process of training the decision tree classifier.

How do you evaluate the accuracy of the model after making predictions?