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在回归问题中把预测值转化为实际值

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  • Ahmad Aladawi  · 技术社区  · 1 年前

    我有一个回归代码,它使用一个热编码器将数据转换为数字。。用于数值和分类值,并预测温度(目标类别)。 我想显示代码的实际预测值,而不是编码值。

    这是代码:

    # Feature Engineering steps (scaling and encoding)
    numerical_cols = data.select_dtypes(include=['number']).columns
    categorical_cols = data.select_dtypes(exclude=['number']).columns
    
    scaler = MinMaxScaler()
    data[numerical_cols] = scaler.fit_transform(data[numerical_cols])
    
    data = pd.get_dummies(data, columns=categorical_cols, drop_first=True)
    
    # Reverse the temperature scaling to get original values
    temperature_scaler = MinMaxScaler()
    temperature = data['Temperature set at home '].values.reshape(-1, 1)
    data['Temperature set at home '] = temperature_scaler.fit_transform(temperature)
    
    # Split the data into features (X) and target variable (y)
    X = data.drop(columns=['Temperature set at home '])
    y = data['Temperature set at home ']
    
    # Split the data into training (X_train) and testing sets (X_test, y_train, y_test)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    

    显示预测的实际值而不是编码的预测值有什么帮助吗?

    谢谢

    1 回复  |  直到 1 年前
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  •   vinayjr    1 年前

    y_pred=reg_model.prdict(X_test) predicted_values=temperature_scaler.inverse_transform(y_pred.整形(-1,1)) 打印(预测值)