- 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()'
来源: http://lib.csdn.net/snippet/machinelearning/49573