Practical Data Science using Python - Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV

The video tutorial explains the use of a random forest classifier to predict credit defaults. It begins with an introduction to the problem of predicting whether a borrower will default on a loan. The tutorial then covers data preparation, including splitting data into training and test sets, and building a random forest model. The model's performance is evaluated, achieving an accuracy of 82%. The tutorial concludes with a discussion on hyperparameter tuning to prevent overfitting, using techniques like grid search and cross-validation.

What is the accuracy of the model after training, and why is it considered high?

What are hyperparameters, and why are they important in the context of random forests?

Explain the concept of overfitting in machine learning models.

What is the purpose of using grid search CV in hyperparameter tuning?