91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
"""
|
|
=================================================
|
|
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.
|
|
|
|
"""
|
|
|
|
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()
|