对于我使用 GB、树和随机森林进行房价数据分析，我的 MSE 太高了

Question

我尝试过使用所有变量，也尝试过选择某些变量，但 MSE 仍然很高。我想知道我的代码是否有错误。我还尝试添加一些功能工程，但暂时将其注释掉，因为它使我的 MSE 变得更糟。

 #kaggle : House Prices - Advanced Regression Techniques   
    import pandas as pd
    import numpy as np
    import sklearn.model_selection
    import matplotlib.pyplot as plt
    import sklearn as sk
    import sklearn.tree
    import sklearn.ensemble
    
    df = pd.read_csv('/Users/andrewhashoush/Downloads/house-prices-advanced-regression-techniques/train.csv')
    df_test = pd.read_csv('/Users/andrewhashoush/Downloads/house-prices-advanced-regression-techniques/test.csv')
    
    print(df.head())
    print(df.info())
    
    #selected_features = ['OverallQual', 'YearBuilt', 'TotalBsmtSF', '1stFlrSF', 'GrLivArea',
    #                     'GarageCars', 'GarageArea', 'MSZoning', 'Neighborhood',
    #                     'KitchenQual', 'CentralAir', 'LotArea', 'MSSubClass', 'LotFrontage', 
    #                     'Street', 'LandContour', 'Utilities', 'OverallCond', 'RoofStyle',
    #                     'RoofMatl', 'BsmtQual','SaleCondition', 'SaleType', 'YrSold', 'MoSold', 
    #                     'PoolArea']
    
    selected_features = [
        'LotFrontage', 'OverallQual', 'OverallCond', 'MasVnrArea', 'HalfBath',
        'BedroomAbvGr', 'KitchenAbvGr', 'GarageCars', 'WoodDeckSF',
        'OpenPorchSF', 'MoSold', 'YrSold', 'MSZoning', 'Alley',
        'LotShape', 'LandContour', 'LotConfig', 'LandSlope', 'Neighborhood',
        'Condition1', 'BldgType', 'HouseStyle', 'RoofStyle', 'Exterior1st',
        'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',
        'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',
        'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',
        'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',
        'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',
        'SaleType', 'SaleCondition'
    ]
    
    #feature engineering
    #df['Quality_Condition'] = df['OverallQual'] * df['OverallCond']
    #df['Age_at_Sale'] = df['YrSold'] - df['YearBuilt']
    #selected_features += ['Quality_Condition', 'Age_at_Sale']
    
    X = df[selected_features]
    print(X.head())
    
    for column in X.columns:
        missing_data = df[column].isnull().sum()
        print(f"{column}: {missing_data}")
      
    categorical_vars = X.select_dtypes(include='object').columns.tolist()
    numerical_vars = X.select_dtypes(exclude='object').columns.tolist()
    
    print("Categorical Variables:", categorical_vars)
    print()
    print("Numerical Variables:", numerical_vars)
    
    
    #%%
    
    #filled the missing categorical values with mode
    for var in categorical_vars:
        mode_value = X[var].mode()[0] 
        # X[var] = X[var].fillna(mode_value) 
        # X.loc[:, var] = X.loc[:, var].fillna(mode_value)
        X.loc[:, var] = X[var].fillna(mode_value)
    
    #filled the missing numerical values with median
    for var in numerical_vars:
        median_value = X[var].median()
        X.loc[:, var] = X[var].fillna(median_value)
    
    #checking it
    for column in X.columns:
        missing_data = X[column].isnull().sum()
        print(f"{column}: {missing_data}")
    
    #one hot encoding 
    X = pd.get_dummies(X, columns=categorical_vars)
    y = df['SalePrice']
    
    #split the data
    X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X,y, train_size =.8, random_state= 123)
    
    dt_model = sklearn.tree.DecisionTreeRegressor(max_depth=5, random_state=123)
    dt_model.fit(X_train, y_train)
    y_pred = dt_model.predict(X_val)  
    mse_val = np.mean((y_pred - y_val)**2)
    print(f"MSE: {mse_val}")
    
    # Random Forest Regressor
    rf_model = sklearn.ensemble.RandomForestRegressor(n_estimators=100, random_state=123)
    rf_model.fit(X_train, y_train)
    y_pred1 = rf_model.predict(X_val)
    mse_val1 = np.mean((y_pred1 - y_val)**2)
    print(f"MSE: {mse_val1}")
    
    # Gradient Boosting Regressor
    gb_model = sklearn.ensemble.GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=123)
    gb_model.fit(X_train, y_train)
    y_pred2 = gb_model.predict(X_val)
    mse_val2 = np.mean((y_pred2 - y_val)**2)
    print(f"MSE: {mse_val2}")

MSE: 1451852149.6361678
MSE: 944388532.5714014
MSE: 755815420.2686024

Answer 1

您的代码中似乎没有任何明显的错误。

不过，您似乎正在尝试根据您正在使用的数据的文件路径来预测房价。对于此任务，这些可能是合理的 MSE 值。

MSE 是平均squared 误差，所以如果你取它的平方根，你会得到root 均方误差。 RMSE 或多或少可以解释为预测中误差的平均绝对值，而 MSE 的解释有点棘手，因为 MSE 的测量单位是目标变量的平方，而 RMSE 的测量单位等于目标变量的平方到目标变量的那些。如果您对所提供的

MSE

值求平方根，您会得到 27,000 到 39,000 之间的值，这并不疯狂 - 换句话说，这意味着您的模型平均偏离了这么多。如果您以美元预测美国房价，那么对于您的模型来说，这是一个相对合理的范围。

更多关于 RMSE 与 MSE 的信息这里