Churn Prediction with Apache Spark Machine Learning

We would like to determine which parameter values of the decision tree produce the best model. A common technique for model selection is k-fold cross validation, where the data is randomly split into k partitions. Each partition is used once as the testing data set, while the rest are used for training. Models are then generated using the training sets and evaluated with the testing sets, resulting in k model performance measurements. The average of the performance scores is often taken to be the overall score of the model, given its build parameters.