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Gradient Boosting Regression in Scikit-learn

Demonstrate Gradient Boosting on the Boston housing dataset.

This example fits a Gradient Boosting model with least squares loss and 500 regression trees of depth 4.

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18.1'

Imports

This tutorial imports shuffle and mean_squared_error.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np
import matplotlib.pyplot as plt

from sklearn import ensemble
from sklearn import datasets
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
Automatically created module for IPython interactive environment

Calculations

Load data

In [3]:
boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]

Fit regression model

In [4]:
params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2,
          'learning_rate': 0.01, 'loss': 'ls'}
clf = ensemble.GradientBoostingRegressor(**params)

clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
print("MSE: %.4f" % mse)
MSE: 6.6706

Plot Training Deviance

In [5]:
test_score = np.zeros((params['n_estimators'],), dtype=np.float64)

for i, y_pred in enumerate(clf.staged_predict(X_test)):
    test_score[i] = clf.loss_(y_test, y_pred)


train = go.Scatter(x=np.arange(params['n_estimators']) + 1, 
                   y=clf.train_score_, 
                   name='Training Set Deviance',
                   mode='lines',
                   line=dict(color='blue')
                  )
test = go.Scatter(x=np.arange(params['n_estimators']) + 1, 
                  y=test_score, 
                  mode='lines',
                  name='Test Set Deviance',
                  line=dict(color='red')
                 )

layout = go.Layout(title='Deviance',
                   xaxis=dict(title='Boosting Iterations'),
                   yaxis=dict(title='Deviance')
                  )
fig = go.Figure(data=[test, train], layout=layout)
In [6]:
py.iplot(fig)
Out[6]:

Plot Feature Importance

In [7]:
feature_importance = clf.feature_importances_

# make importances relative to max importance
feature_importance = 100.0 * (feature_importance / feature_importance.max())
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[0]) + .5

trace = go.Bar(x=feature_importance[sorted_idx],
               y=boston.feature_names[sorted_idx],
               orientation = 'h'
              )

layout = go.Layout(xaxis=dict(title='Relative Importance'),
                   yaxis=dict(title='Variable Importance')
                  )
fig = go.Figure(data=[trace], layout=layout)
In [8]:
py.iplot(fig)
Out[8]:

License

Author:

    Peter Prettenhofer <peter.prettenhofer@gmail.com>

License:

    BSD 3 clause
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