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
1.1.1. Ordinary Least Squares最小二乘法
>>> 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:
', 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()