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"""
=================================================
Plot individual and voting regression predictions
=================================================
.. currentmodule:: sklearn
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:
:class:`~ensemble.GradientBoostingRegressor`,
:class:`~ensemble.RandomForestRegressor`, and
:class:`~linear_model.LinearRegression`).
Then the above 3 regressors will be used for the
:class:`~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.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes
from sklearn.ensemble import (
GradientBoostingRegressor,
RandomForestRegressor,
VotingRegressor,
)
from sklearn.linear_model import LinearRegression
# %%
# 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)
# %%
# 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 :class:`~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()
|
