Cross-validation scoring

Now, you should use cross-validation scoring with cross_val_score() to check the overall performance.

This is exercise presents an excellent opportunity to test out the use of the hyperparameters learning_rate and max_depth. Remember, hyperparameters are like settings which can help create optimum performance.

The data sets cr_loan_prep, X_train, and y_train have already been loaded in the workspace.

This exercise is part of the course

Credit Risk Modeling in Python

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Exercise instructions

Create a gradient boosted tree with a learning rate of 0.1 and a max depth of 7. Store the model as gbt.
Calculate the cross validation scores against the X_train and y_train data sets with 4 folds. Store the results as cv_scores.
Print the cross validation scores.
Print the average accuracy score and standard deviation with formatting.

Hands-on interactive exercise

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

# Create a gradient boosted tree model using two hyperparameters
____ = xgb.____(____ = ____, ____ = ____)

# Calculate the cross validation scores for 4 folds
____ = ____(____, ____, np.ravel(____), cv = ____)

# Print the cross validation scores
print(____)

# Print the average accuracy and standard deviation of the scores
print("Average accuracy: %0.2f (+/- %0.2f)" % (____.____(),
                                              ____.____() * 2))

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