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 are hyperparameters in the context of decision trees?

Explain the purpose of the 'Max features' hyperparameter.

What does the 'Max depth' parameter control in a decision tree?

Describe the function of 'min samples split' in decision tree pruning.

How does 'Grid search CV' help in optimizing hyperparameters?

What is the significance of 'K Fold cross validation' in model training?

Explain how the results of the grid search CV can be interpreted.

What are the potential outcomes of tuning the 'mean samples leaf' parameter?

Discuss the impact of overfitting in decision trees and how it can be mitigated.

What is the role of the 'criterion' parameter in decision trees?