在回归问题中将预测值转换为实际值

Question

我有一个回归代码，它获取数据并使用 one-hot 编码器将其转换为数值，.. 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的值。

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)

有任何帮助来显示预测的实际值而不是编码的预测值吗？

谢谢！

Answer 1

y_pred = reg_model.predict(X_test) 预测值=温度缩放器.inverse_transform(y_pred.reshape(-1,1)) 打印（预测值）