Measuring accuracy

You'll now practice using XGBoost's learning API through its baked in cross-validation capabilities. As Sergey discussed in the previous video, XGBoost gets its lauded performance and efficiency gains by utilizing its own optimized data structure for datasets called a DMatrix.

In the previous exercise, the input datasets were converted into DMatrix data on the fly, but when you use the xgboost cv object, you have to first explicitly convert your data into a DMatrix. So, that's what you will do here before running cross-validation on churn_data.

Diese Übung ist Teil des Kurses

Extreme Gradient Boosting with XGBoost

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Anleitung zur Übung

Create a DMatrix called churn_dmatrix from churn_data using xgb.DMatrix(). The features are available in X and the labels in y.
Perform 3-fold cross-validation by calling xgb.cv(). dtrain is your churn_dmatrix, params is your parameter dictionary, nfold is the number of cross-validation folds (3), num_boost_round is the number of trees we want to build (5), metrics is the metric you want to compute (this will be "error", which we will convert to an accuracy).

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Create arrays for the features and the target: X, y
X, y = churn_data.iloc[:,:-1], churn_data.iloc[:,-1]

# Create the DMatrix from X and y: churn_dmatrix
churn_dmatrix = ____(data=____, label=____)

# Create the parameter dictionary: params
params = {"objective":"reg:logistic", "max_depth":3}

# Perform cross-validation: cv_results
cv_results = ____(dtrain=____, params=____, 
                  nfold=____, num_boost_round=____, 
                  metrics="____", as_pandas=____, seed=123)

# Print cv_results
print(cv_results)

# Print the accuracy
print(((1-cv_results["test-error-mean"]).iloc[-1]))

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