转发说明：by majunman    from HIT    email:2192483210@qq.com

简介：scikit-learn是数据挖掘和数据分析的有效工具，它建立在 NumPy, SciPy, and matplotlib基础上。开源的但商业不允许

1. Supervised learning

1.1. Generalized Linear Models

>>> from sklearn import linear_model

reg = linear_model.LinearRegression()
reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
reg.coef_
array([ 0.5, 0.5])

reg-http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression

reg.coef_   是回归函数的结果，即相关系数

具体实验：

print(__doc__)

Code source: Jaques Grobler

License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score

Load the diabetes dataset

diabetes = datasets.load_diabetes() #加载diabetes数据集（sklearn提供的几种数据集之一，该数据是糖尿病数据集）

Use only one feature

diabetes_X = diabetes.data[:, np.newaxis, 2] #只加载一个特征值

Split the data into training/testing sets

diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

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)

Make predictions using the testing set

diabetes_y_pred = regr.predict(diabetes_X_test)

The coefficients

print('Coefficients: \n', regr.coef_)

The mean squared error

print("Mean squared error: %.2f"
% mean_squared_error(diabetes_y_test, diabetes_y_pred))

Explained variance score: 1 is perfect prediction

print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))

Plot outputs

plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()