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Plot individual and voting regression predictions¶
Plot individual and averaged regression predictions for Boston dataset.
First, three exemplary regressors are initialized
(GradientBoostingRegressor,
RandomForestRegressor, and
LinearRegression) and used to initialize a
VotingRegressor.
The red starred dots are the averaged predictions.
print(__doc__)
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import VotingRegressor
# Loading some example data
X, y = datasets.load_boston(return_X_y=True)
# Training classifiers
reg1 = GradientBoostingRegressor(random_state=1, n_estimators=10)
reg2 = RandomForestRegressor(random_state=1, n_estimators=10)
reg3 = LinearRegression()
ereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])
reg1.fit(X, y)
reg2.fit(X, y)
reg3.fit(X, y)
ereg.fit(X, y)
xt = X[:20]
plt.figure()
plt.plot(reg1.predict(xt), 'gd', label='GradientBoostingRegressor')
plt.plot(reg2.predict(xt), 'b^', label='RandomForestRegressor')
plt.plot(reg3.predict(xt), 'ys', label='LinearRegression')
plt.plot(ereg.predict(xt), 'r*', 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('Comparison of individual predictions with averaged')
plt.show()
Total running time of the script: ( 0 minutes 0.461 seconds)
Estimated memory usage: 8 MB
