List Comprehension Practice (수정 보충 필요)

from sklearn.ensemble import RandomForestRegressor

# Define the models
model_1 = RandomForestRegressor(n_estimators=50, random_state=0)
model_2 = RandomForestRegressor(n_estimators=100, random_state=0)
model_3 = RandomForestRegressor(n_estimators=100, criterion='mae', random_state=0)
model_4 = RandomForestRegressor(n_estimators=200, min_samples_split=20, random_state=0)
model_5 = RandomForestRegressor(n_estimators=100, max_depth=7, random_state=0)

models = [model_1, model_2, model_3, model_4, model_5]

from sklearn.metrics import mean_absolute_error

# Function for comparing different models
def score_model(model, X_t=X_train, X_v=X_valid, y_t=y_train, y_v=y_valid):
    model.fit(X_t, y_t)
    preds = model.predict(X_v)
    return mean_absolute_error(y_v, preds)

for i in range(0, len(models)):
    mae = score_model(models[i])
    print("Model %d MAE: %d" % (i+1, mae))
    
# Fill in the best model
res = 0
for i, model in enumerate(models):
    if score_model(models[i])<score_model(models[res]):
        res = i
best_model = models[res]

여기서 마지막 best_model을 찾는 부분을 list_comprehension으로 구현할 수는 없을까?