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  • 【线性回归】波士顿房价预测

    from sklearn import datasets # 数据集
    from sklearn.model_selection import train_test_split
    from sklearn import linear_model
    import matplotlib.pyplot as plt 
    boston = datasets.load_boston() # 波士顿房价数据
    boston
    # 创建训练集 与 测试集
    x_train,x_test,y_train,y_test = train_test_split(boston.data,boston.target,test_size=0.1,random_state=42)
    print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)

     # 训练数据
     linreg = linear_model.LinearRegression()
     linreg.fit(x_train, y_train)

     # 得出预测值

     y_pred = linreg.predict(x_test)
     y_pred

     
    plt.figure(figsize=(10,6)) # 设置大小
    
    plt.plot(y_test,linewidth=3,label='Actual') 
    plt.plot(y_pred,linewidth=3,label='Prediction')
    
    # 显示上面设置的名字与底部
    plt.legend(loc='best')
    plt.xlabel('test data point')
    plt.ylabel('target value')
    
    
    
    
    

    plt.plot(y_test,y_pred,'o')
    plt.plot([-10,60],[-10,60],'k--')
    plt.axis([-10,60,-10,60])

    
    

    plt.xlabel('Actual')
    plt.ylabel('Prediction')

     

    有道词典
    # 训练数据 linreg = ...
    详细X
    # training data Linreg = linear_model. LinearRegression () Linreg. Fit (x_train y_train)
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  • 原文地址:https://www.cnblogs.com/wanghong1994/p/13446788.html
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