Plotting Cross-Validated PredictionsΒΆ
This example shows how to use cross_val_predict to visualize prediction errors.
Python source code: plot_cv_predict.py
from sklearn import datasets
from sklearn.cross_validation import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt
lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target
# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validated:
predicted = cross_val_predict(lr, boston.data, y, cv=10)
fig,ax = plt.subplots()
ax.scatter(y, predicted)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
fig.show()
Total running time of the example: 6.30 seconds ( 0 minutes 6.30 seconds)