Practical Data Science using Python - Decision Tree - Model Optimization using Grid Search Cross Validation

The video tutorial explains the concept of hyperparameters in decision trees and introduces Grid Search CV with K-Fold Cross Validation as a method to optimize these parameters. It details the implementation process, including setting up the parameter grid and analyzing results. The tutorial also covers tuning multiple parameters simultaneously and evaluating the final model's performance.

What is the purpose of hyperparameters in a decision tree?

• To increase the number of features

• To control the tree's growth and ensure optimal splits

• To enhance the tree's visual representation

• To decrease the number of observations

Which of the following is NOT a hyperparameter in decision trees?

• Max accuracy

• Max depth

• Max features

• Min samples split

What does Grid Search CV help achieve in decision tree modeling?

• It visualizes the decision tree

• It finds the optimal hyperparameters

• It increases the number of features

• It reduces the dataset size

How does K-Fold Cross Validation work?

• It visualizes the decision tree

• It reduces the number of observations

• It increases the number of features in the dataset

• It splits the dataset into random segments for training and testing

What is the first step in implementing Grid Search CV?

• Reducing the dataset size

• Importing necessary modules

• Visualizing the decision tree

• Increasing the number of features

What does the grid search process involve?

• Reducing the dataset size

• Increasing the number of features

• Running the decision tree model multiple times with different parameter values

• Visualizing the decision tree

What is the significance of plotting accuracies for different max depth values?

• To increase the number of features

• To reduce the dataset size

• To identify the optimal max depth for the decision tree

• To visualize the decision tree

Which hyperparameter was found to be optimal at a value of 4?

• Max features

• Max leaf nodes

• Min samples split

• Max depth

What was the final accuracy achieved by the decision tree model after tuning?

• 98%

• 95.5%

• 92%

• 90%

Why is hyperparameter tuning important in decision tree modeling?

• To increase the number of features

• To achieve optimal results

• To visualize the decision tree

• To reduce the dataset size