# Plot individual and voting regression predictions¶

A voting regressor is an ensemble meta-estimator that fits base regressors each on the whole dataset. It, then, averages the individual predictions to form a final prediction. We will use three different regressors to predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression). Then, using them we will make voting regressor VotingRegressor.

Finally, we will plot all of them for comparison.

We will work with the diabetes dataset which consists of the 10 features collected from a cohort of diabetes patients. The target is the disease progression after one year from the baseline.

print(__doc__)

import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import VotingRegressor


## Training classifiers¶

First, we are going to load diabetes dataset and initiate gradient boosting regressor, random forest regressor and linear regression. Next, we are going to use each of them to build the voting regressor:

X, y = datasets.load_diabetes(return_X_y=True)

# Train classifiers
reg2 = RandomForestRegressor(random_state=1)
reg3 = LinearRegression()

reg1.fit(X, y)
reg2.fit(X, y)
reg3.fit(X, y)

ereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])
ereg.fit(X, y)


Out:

VotingRegressor(estimators=[('gb', GradientBoostingRegressor(random_state=1)),
('rf', RandomForestRegressor(random_state=1)),
('lr', LinearRegression())])


## Making predictions¶

Now we will use each of the regressors to make 20 first predictions about the diabetes dataset.

xt = X[:20]

pred1 = reg1.predict(xt)
pred2 = reg2.predict(xt)
pred3 = reg3.predict(xt)
pred4 = ereg.predict(xt)


## Plot the results¶

Finally, we will visualize the 20 predictions. The red stars show the average prediction

plt.figure()
plt.plot(pred2, 'b^', label='RandomForestRegressor')
plt.plot(pred3, 'ys', label='LinearRegression')
plt.plot(pred4, 'r*', ms=10, label='VotingRegressor')

plt.tick_params(axis='x', which='both', bottom=False, top=False,
labelbottom=False)
plt.ylabel('predicted')
plt.xlabel('training samples')
plt.legend(loc="best")
plt.title('Regressor predictions and their average')

plt.show()


Total running time of the script: ( 0 minutes 0.936 seconds)

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