线性回归:寻找⼀一种能预测的趋势
回归问题的条件/前提:
1) 收集的数据
2) 假设的模型,即一个函数,这个函数里含有未知的参数,通过学习,可以估计出参数。然后利用这个模型去预测/分类新的数据。
案例:
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)
# 预测房价结果
model = joblib.load("./tmp/test.pkl")
y_predict = std_y.inverse_transform(model.predict(x_test))
print("保存的模型预测的结果:", y_predict)
# 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_)
# 保存训练好的模型
# joblib.dump(lr, "./tmp/test.pkl")
# # 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))
#
# # 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