Gradient Boosting

Gradient boosting is typically used with decision trees (especially CART regression trees) of a fixed size as base learners. For this special case Friedman proposes a modification to gradient boosting method which improves the quality of fit of each base learner.

from miml import datasets
from miml.regression import GradientTreeBoost

fn = os.path.join(datasets.get_data_home(), 'regression',
    'diabetes.csv')
df = DataFrame.read_table(fn, delimiter=',',
    format='%64f', index_col=0)

x = df.values
y = array(df.index.data)

model = GradientTreeBoost()
model.fit(x, y)

print(model.predict(x[:10,:]))

>>> run script...
array([194.7389360248372, 75.38935152901469, 167.1564225597021, 192.1702512661937, 99.397482512624, 100.2975566989432, 82.72846455873852, 91.96946209282093, 114.3010663633844, 219.91127834377713])