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hub / github.com/capitalk/treelearn / train_random_forest

Function train_random_forest

treelearn/recipes.py:31–93  ·  view source on GitHub ↗

A random forest is a bagging ensemble of randomized trees, so it can be implemented by combining the BaggedClassifier and RandomizedTree objects. This function is just a helper to your life easier. Parameters ---------- X : numpy array containing input data. Should h

(
        X, 
        Y, 
        num_trees = 20, 
        max_thresholds = 10, 
        max_height = None, 
        min_leaf_size = None, 
        bagging_percent=0.65)

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Callers 1

test_simple_forestFunction · 0.90

Calls 4

RandomizedTreeClass · 0.90
RegressionEnsembleClass · 0.90
ClassifierEnsembleClass · 0.90
fitMethod · 0.45

Tested by 1

test_simple_forestFunction · 0.72