6회 기출 작업형2 오류 파악

import pandas as pd
train = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/refs/heads/main/p4/6_2/energy_train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/refs/heads/main/p4/6_2/energy_test.csv")


X_train = train.drop(['Heat_Load'], axis = 1)
y_train = train['Heat_Load']
X_test = test

from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
X_train['Roof'] = encoder.fit_transform(X_train['Roof'])
X_test['Roof'] = encoder.transform(X_test['Roof'])
X_train['Height'] = encoder.fit_transform(X_train['Height'])
X_test['Height'] = encoder.transform(X_test['Height'])
X_train['Orient'] = encoder.fit_transform(X_train['Orient'])
X_test['Orient'] = encoder.transform(X_test['Orient'])

num_columns = X_train.select_dtypes(exclude = 'object').columns
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train[num_columns] = scaler.fit_transform(X_train[num_columns])
X_test[num_columns] = scaler.transform(X_test[num_columns])

from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size =0.2, random_state=2025)
print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
model.fit(X_train, y_train)
y_val_pred = model.predict(X_val)

 ValueError: could not convert string to float: 'High'

위와 같이 작성하였는데 해당오류가 뜹니다. 라벨인코딩은 진행하였는데 왜 오류가 발생할까요??.. 원핫인코딩을 진행해야하나요?

   from sklearn.preprocessing import OneHotEncoder

   encoder = OneHotEncoder(drop='first', sparse=False)
   X_train_encoded = encoder.fit_transform(X_train[['Roof', 'Height', 'Orient']])
   X_test_encoded = encoder.transform(X_test[['Roof', 'Height', 'Orient']])

   X_train = X_train.drop(['Roof', 'Height', 'Orient'], axis=1)
   X_test = X_test.drop(['Roof', 'Height', 'Orient'], axis=1)

   X_train = pd.concat([X_train, pd.DataFrame(X_train_encoded)], axis=1)
   X_test = pd.concat([X_test, pd.DataFrame(X_test_encoded)], axis=1)

   X_train = pd.get_dummies(X_train, columns=['Roof', 'Height', 'Orient'], drop_first=True)
   X_test = pd.get_dummies(X_test, columns=['Roof', 'Height', 'Orient'], drop_first=True)