【机器学习实战】线性回归分析

本实验基于python语言，采用sklearn工具包进行线性回归实验。实验内容如下：
1，录入课本上关于房价预测的例程，并运行得出结果；
2，通过查找ptyhon编程资料，对给出的例程按要求进行修改，得出新的实验结果；
通过本实验应掌握如下内容：
1，训练样本与测试样本的随机选取；
2，通过sklearn工具包对数据进行线性回归拟合；
3，学会输出线性回归的统计量指标。

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_X, diabetes_y = datasets.load_diabetes(return_X_y=True)# Use only one feature
diabetes_X = diabetes_X[:, np.newaxis, 3]# 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_y[:-20]
diabetes_y_test = diabetes_y[-20:]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))
# The coefficient of determination: 1 is perfect prediction
print("Coefficient of determination: %.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()