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Decision Tree Regression with AdaBoost in Scikit-learn

A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tree regressor. As the number of boosts is increased the regressor can fit more detail.

[1] H.Drucker, “Improving Regressors using Boosting Techniques”, 1997.

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Version

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

Imports

This tutorial imports DecisionTreeRegressor and AdaBoostRegressor.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools

import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
Automatically created module for IPython interactive environment

Calculations

In [3]:
rng = np.random.RandomState(1)
X = np.linspace(0, 6, 100)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])

# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=4)

regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
                          n_estimators=300, random_state=rng)

regr_1.fit(X, y)
regr_2.fit(X, y)

# Predict
y_1 = regr_1.predict(X)
y_2 = regr_2.predict(X)

Plot Results

In [4]:
def data_to_plotly(x):
    plotly_data = []
    for i in range(0, len(x)):
        plotly_data.append(x[i][0])
        
    return plotly_data
In [5]:
training_samples = go.Scatter(x=data_to_plotly(X), 
                              y=y, 
                              name="training samples",
                              mode='markers',
                              marker=dict(color='black', size=6)
                             )

n_estimator1 = go.Scatter(x=data_to_plotly(X), 
                          y=y_1,
                          name="n_estimators=1",
                          mode='lines',
                          line=dict(color='green'), 
                          )

n_estimator300 = go.Scatter(x=data_to_plotly(X),
                            y=y_2, 
                            name="n_estimators=300",
                            mode='lines',
                            line=dict(color='red'), 
                           )
data = [training_samples, n_estimator1, n_estimator300]

layout = go.Layout(title='Boosted Decision Tree Regression',
                   xaxis=dict(title='data'),
                   yaxis=dict(title='target')
                  )
fig = go.Figure(data=data, layout=layout)
In [6]:
py.iplot(fig)
Out[6]:

License

Author:

    Noel Dawe <noel.dawe@gmail.com>

License:

    BSD 3 clause
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