在回归问题中把预测值转化为实际值

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