Ensemble meta-estimator

Source: scikit learn

A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction.

We will use three different regressors to predict the data:

• ensemble.GradientBoostingRegressor,

• ensemble.RandomForestRegressor, and

• linear_model.LinearRegression.

Then the above 3 regressors will be used for the

• ensemble.VotingRegressor.

Finally, we will plot the predictions made by all models for comparison.

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

%matplotlib inline

import matplotlib.pyplot as plt

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

Training classifiers

First, we will load the diabetes dataset and initiate a gradient boosting regressor, a random forest regressor and a linear regression. Next, we will use the 3 regressors to build the voting regressor:

X, y = load_diabetes(return_X_y=True)

# Train classifiers
reg1 = GradientBoostingRegressor(random_state=1)
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)

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 the 20 first predictions.

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 made by ~ensemble.VotingRegressor.

plt.figure()
plt.plot(pred1, "gd", label="GradientBoostingRegressor")
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()