How to create a Classification tree using Shogun?

Recipe Objective

This recipe explains how to make a classification tree using shogun
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Classification Tree

Decision tree learning maps observations about an item by using a decision tree as a predictive model to conclude about the item's target value.
Decision trees can be used as the classification tree, where the predicted outcome can be considered a real number.
Classification And Regression Tree (CART) algorithm is a joint method that we can apply to the classification tree.
In the below example we have applied Classification And Regression Tree (CART) algorithm in a multi-class dataset to predict the labels.

x_train = RealFeatures(feats_train)
x_test = RealFeatures(feats_test)
y_train = MulticlassLabels(labels_train)
y_test = MulticlassLabels(labels_test)

classifier.train(x_train)
predict = classifier.apply_multiclass(x_test)

eval = MulticlassAccuracy()
accuracy = eval.evaluate(predict, y_test)