【机器学习系列】scikit-learn中的Linear Regression Example

说明：

这里为了以后方面查阅浏览，只搬运了别人的基本代码，相关细节可查看其它资料。例如：

http://scikit-learn.org/0.11/auto_examples/linear_model/plot_ols.html

代码：

print __doc__


# Code source: Jaques Grobler
# License: BSD


import pylab as pl
import numpy as np
from sklearn import datasets, linear_model

# Load the diabetes dataset
diabetes = datasets.load_diabetes()


# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis]
diabetes_X_temp = diabetes_X[:, :, 2]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X_temp[:-20]
diabetes_X_test = diabetes_X_temp[-20:]

from sklearn.datasets.samples_generator import make_regression

# this is our test set, it's just a straight line with some
# gaussian noise
X, Y = make_regression(n_samples=100, n_features=1, n_informative=1,\
                        random_state=0, noise=35)


# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# The coefficients
print 'Coefficients: \n', regr.coef_
# The mean square error
print ("Residual sum of squares: %.2f" %
        np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print ('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))

# Plot outputs
pl.scatter(diabetes_X_test, diabetes_y_test,  color='black')
pl.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
        linewidth=3)

pl.xticks(())
pl.yticks(())

pl.show()

效果图：