一、题设
线性回归是最简单和经典的回归模型。 假设输入xx为dd维,预测目标yy为连续型取值。线性回归的模型形式为:y=w0+w1x1+…+wdxdy=w0+w1x1+…+wdxd
下面我们通过线性回归构建房价预测模型,回归系数使用最小二乘法来估计。 我们的案例基于波士顿房价数据集,使用sklearn.linear_model模块提供的线性回归函数,构建房价预测模型,预测房价。
1.样本数:506
2.特征数量:13个连续特征(包括“类”属性“MEDV”),1个二进制值特征。
sklearn.linear_model模块包含了常见的线性模型,即“预测目标能够表示成输入变量的线性组合形式”的模型。
在我们的例子中 y = 波士顿房价。x = 其余的输入变量。 首先,我们导入sklearn的linear_model模块。 然后将预测变量从DataFrame中删除。 最后创建一个线性模型对象mode。
二、DATASET
bd网盘链接: /s/1YQvi5hf8iJ3CPtvSAZYQWg
提取码: 8888
Boston_housing_data.csv
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二、基本思路
首先读取csv文件,即所给的dataset,其次对数据进行简单的处理:1.抽出标签target形成x。2.移除target将剩下的数据形成y。3.利用分隔函数将数据集分为训练集和测试集,七三分之。再次,引入一个线性回归模型,使用训练集的数据对模型进行训练。之后使用训练好的模型对测试集的x数据进行预测出y_predict。最后计算y_predict与测试集中的y的一些误差,从而评估模型的优劣。
三、代码实现
import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import r2_scorefrom sklearn.metrics import mean_squared_error# 读csv文件,即Datasetdata = pd.read_csv('boston_housing_data.csv')# 抽出标签targety = data['TARGET']# 将标签外的另外的数据保存x = data.copy().drop(['TARGET'],axis = 1)# 利用分隔函数将数据集分割为训练集和测试集,分割点在0.3处x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)# 引入一个线性回归模型mode = LinearRegression()# 使用训练集的数据训练模型mode.fit(x_train,y_train)# 用模型测试3/10的测试集数据形成标签y_predict = mode.predict(x_test)# 将预测标签与真实标签进行性能的比较# 使用均值作为误差基准,看预测误差是否大于或者小于均值基准误差r2 = r2_score(y_test,y_predict)# 均方误差hit = mean_squared_error(y_test,y_predict)print(r2,hit)