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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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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