def choose_feature_to_split(dataset):
"""
Choose the root to be the feature which has the highest information
gain.
Args:
dataset: model.DataSet
The data set being used to build the decision tree.
Returns:
feature: string
The feature which should be the root.
"""
gains = {}
for feature in dataset.feature_list():
gains[feature] = info_gain(feature, dataset)
return get_key_with_highest_value(gains)
def choose_feature_to_split(dataset):
"""
Choose the root to be the feature which has the highest information
gain.
Args:
dataset: model.DataSet
The data set being used to build the decision tree.
Returns:
feature: string
The feature which should be the root.
"""
gains = {}
for feature in dataset.feature_list():
gains[feature] = info_gain(feature, dataset)
return get_key_with_highest_value(gains)
def test_info_gain(self):
dataset = self.create_example_dataset()
self.assertAlmostEqual(info_gain("A", dataset), 0.311, places=3)
def test_info_gain(self):
dataset = self.create_example_dataset()
self.assertAlmostEqual(info_gain("A", dataset), 0.311, places=3)
