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%.

What is the criterion used in the decision tree classifier in this tutorial?

• Gini

• Information Gain

• Entropy

• Variance

What is the maximum depth specified for the decision tree in this tutorial?

• 2

• 3

• 5

• 4

Which function is used to train the decision tree classifier with the dataset?

• fit

• evaluate

• train

• predict

What is the purpose of the 'fit' function in the context of decision trees?

• To create a new decision tree

• To train the model with data

• To visualize the decision tree

• To evaluate the model's accuracy

What is the accuracy of the decision tree model as mentioned in the tutorial?

• 70%

• 75%

• 85%

• 80%