How to implement random forest in shogun

In this recipe, we shall learn how can we implement a random forest model in shogun. We shall also take a look at an example for better clarity.

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Recipe Objective: How to implement random forest in shogun?

This recipe explains how to implement random forest in shogun.
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Random Forest

Random forest constitutes various individual decision trees that operate as an ensemble. These individual trees in random forest divide class prediction, and the class which gains the maximum votes becomes the model's prediction.

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

vote = MajorityVote()
rf = RandomForest(x_train, l_train, 100)
rf.set_combination_rule(vote)

rf.train()
predict = rf.apply_multiclass(x_test)
accuracy = MulticlassAccuracy()
acc = accuracy.evaluate(predict, y_test)

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