1. 回归

回归的一个重要特征就是，其目标值是连续的。本节主要介绍了线性回归和岭回归的实例。

1.1 线性回归实例

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor,  Ridge, LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, classification_report
from sklearn.externals import joblib
import pandas as pd
import numpy as np

def mylinear():
    """
    线性回归直接预测房子价格
    :return: None
    """
    # 获取数据
    lb = load_boston()

    # 分割数据集到训练集和测试集
    x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25)

    print(y_train, y_test)

    # 进行标准化处理(?) 目标值处理？
    # 特征值和目标值是都必须进行标准化处理, 实例化两个标准化API
    std_x = StandardScaler()

    x_train = std_x.fit_transform(x_train)
    x_test = std_x.transform(x_test)

    # 目标值
    std_y = StandardScaler()

    y_train = std_y.fit_transform(y_train)
    y_test = std_y.transform(y_test)

    # estimator预测
    # 正规方程求解方式预测结果
    lr = LinearRegression()
    
    lr.fit(x_train, y_train)
    
    print(lr.coef_)

    # 预测测试集的房子价格
    y_lr_predict = std_y.inverse_transform(lr.predict(x_test))
    
    print("正规方程测试集里面每个房子的预测价格：",      y_lr_predict)
    
    print("正规方程的均方误差：",    mean_squared_error(std_y.inverse_transform(y_test), y_lr_predict))
    
    # 梯度下降去进行房价预测
    sgd = SGDRegressor()
    
    sgd.fit(x_train, y_train)
    
    print(sgd.coef_)
    
    # 预测测试集的房子价格
    y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test))
    
    print("梯度下降测试集里面每个房子的预测价格：",      y_sgd_predict)
    
    print("梯度下降的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict))
     return None

if __name__ == "__main__":
    mylinear()

1.2 岭回归

岭回归可以解决多重共线性问题，也可以解决过拟合问题。（但是，岭回归无法像LASSO或者SCAD算法实现变量选择）

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor,  Ridge, LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, classification_report
from sklearn.externals import joblib
import pandas as pd
import numpy as np

def mylinear():
    """
    线性回归直接预测房子价格
    :return: None
    """
    # 获取数据
    lb = load_boston()

    # 分割数据集到训练集和测试集
    x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25)

    print(y_train, y_test)

    # 进行标准化处理(?) 目标值处理？
    # 特征值和目标值是都必须进行标准化处理, 实例化两个标准化API
    std_x = StandardScaler()

    x_train = std_x.fit_transform(x_train)
    x_test = std_x.transform(x_test)

    # 目标值
    std_y = StandardScaler()

    y_train = std_y.fit_transform(y_train)
    y_test = std_y.transform(y_test)
    
    # 岭回归去进行房价预测
    rd = Ridge(alpha=1.0)
    
    rd.fit(x_train, y_train)
    
    print(rd.coef_)
    
    # 预测测试集的房子价格
    y_rd_predict = std_y.inverse_transform(rd.predict(x_test))
    
    print("梯度下降测试集里面每个房子的预测价格：", y_rd_predict)
    
    print("梯度下降的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), y_rd_predict))

    return None

if __name__ == "__main__":
    mylinear()