Measuring AUC

Now that you've used cross-validation to compute average out-of-sample accuracy (after converting from an error), it's very easy to compute any other metric you might be interested in. All you have to do is pass it (or a list of metrics) in as an argument to the metrics parameter of xgb.cv().

Your job in this exercise is to compute another common metric used in binary classification - the area under the curve ("auc"). As before, churn_data is available in your workspace, along with the DMatrix churn_dmatrix and parameter dictionary params.

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Extreme Gradient Boosting with XGBoost

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Instrucciones del ejercicio

Perform 3-fold cross-validation with 5 boosting rounds and "auc" as your metric.
Print the "test-auc-mean" column of cv_results.

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# Perform cross_validation: cv_results
cv_results = ____(dtrain=____, params=____, 
                  nfold=____, num_boost_round=____, 
                  metrics="____", as_pandas=True, seed=123)

# Print cv_results
print(cv_results)

# Print the AUC
print((cv_results["____"]).iloc[-1])

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