How to implement Random Forest using shogun?

This recipe helps you to implement Random Forest using shogun.

Recipe Objective

This recipe explains how we can implement Random Forest using shogun.
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Apply Machine Learning to Demand Forecasting Data Science Problems

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)

y = MajorityVote()
z = RandomForest(x_train, y_train, 100)
z.set_combination_rule(y)

z.train()
predict = rand_forest.apply_multiclass(x_test)

eval = MulticlassAccuracy()
oob = rand_forest.get_oob_error(eval)
accuracy = eval.evaluate(predict, y_test)

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