An example showing how to combine py-earth with two of scikit-learn's meta-regressors, AdaBoostRegressor and GridSearchCV.

pyearth_with_sklearn.py

from sklearn.ensemble import AdaBoostRegressor
from sklearn.metrics import r2_score
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from pyearth import Earth
import numpy as np
import pandas as pd

# Generate a data set
np.random.seed(1)
n = 10000
X = pd.DataFrame(np.random.uniform(0.,10.,size=n),columns=['x0'])
X['x1'] = np.random.binomial(1,.01,size=n)
X['x2'] = np.random.uniform(0.,10.,size=n)
X['x3'] = np.random.binomial(1,.01,size=n)
X['x4'] = np.random.uniform(0.,10.,size=n)
y = 10. * np.abs(X['x0'] - 10.) - \
    20. * X['x1'] * (np.abs(X['x2']) + X['x3'] * np.abs(X['x4'])) + \
    5. * np.random.normal(size=n)

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=1)

# Fit the basic Earth model
print 'Fitting basic Earth model...'
earth_model = Earth().fit(X_train,y_train)
print 'Done.'

# Fit the boosted Earth model
print 'Fitting boosted Earth model...'
boosted_earth_model = AdaBoostRegressor(base_estimator=Earth()).fit(X_train,y_train)
print 'Done.'

# Fit the grid searched Earth model
print 'Fitting grid searched Earth model...'
param_grid = [{'max_degree': [1,2,3,4], 'allow_linear': [False, True], 'penalty': [0.,1.,2.,3.,4.,5.,6.]}]
grid_search_earth_model = GridSearchCV(Earth(),param_grid).fit(X_train, y_train)
print 'Done.'

print 'Earth Score: %f' % r2_score(y_test, earth_model.predict(X_test))
print 'Boosted Earth Score: %f' % r2_score(y_test, boosted_earth_model.predict(X_test))
print 'Grid Searched Earth Score: %f' % r2_score(y_test, grid_search_earth_model.predict(X_test))