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?

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

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Explain the concept of overfitting in machine learning models.

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

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