Gradient boosted portfolio performance

At this point you've looked at predicting probability of default using both a LogisticRegression() and XGBClassifier(). You've looked at some scoring and have seen samples of the predictions, but what is the overall affect on portfolio performance? Try using expected loss as a scenario to express the importance of testing different models.

A data frame called portfolio has been created to combine the probabilities of default for both models, the loss given default (assume 20% for now), and the loan_amnt which will be assumed to be the exposure at default.

The data frame cr_loan_prep along with the X_train and y_train training sets have been loaded into the workspace.

This exercise is part of the course

Credit Risk Modeling in Python

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Exercise instructions

Print the first five rows of portfolio.
Create the expected_loss column for the gbt and lr model named gbt_expected_loss and lr_expected_loss.
Print the sum of lr_expected_loss for the entire portfolio.
Print the sum of gbt_expected_loss for the entire portfolio.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print the first five rows of the portfolio data frame
print(____.____())

# Create expected loss columns for each model using the formula
portfolio[____] = portfolio[____] * portfolio[____] * portfolio[____]
portfolio[____] = portfolio[____] * portfolio[____] * portfolio[____]

# Print the sum of the expected loss for lr
print('LR expected loss: ', np.____(____[____]))

# Print the sum of the expected loss for gbt
print('GBT expected loss: ', np.____(____[____]))

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