Gradient-Boosted Trees are ensembles of decision trees. Gradient-Boosted Trees iteratively train decision trees to minimize loss function. Gradient-Boosted Trees handle categorical features, support multiclass classification, and don't require feature scaling.
Spark ML implements Gradient-Boosted Trees using the existing decision tree implementation. It supports both classification and regression.
Let's train the StumbleUpon dataset by splitting it into 80% training and 20% testing, use Gradient-Boosted Trees from Spark to build the model, and get evaluation metrics around the test data as follows:
// split data randomly into training and testing dataset val Array(training, test) = dataFrame.randomSplit(Array(0.8, ...