Кросс-валидация в Python

Нужно получить кросс-валидацию на трёх равных по размеру блоках. Делаю так и получаю ошибку "ValueError: Expected 2D array, got 1D array instead":

import pandas as pd
from sklearn.tree import DecisionTreeClassifier

data = pd.read_csv('/datasets/heart.csv')
features = data.drop(['target'], axis=1)
target = data['target']

scores = []

sample_size = int(len(data)/3)

for i in range(0, len(data), sample_size):
    valid_indexes = data.iloc[i: i + sample_size].index
    train_indexes = (data.iloc[:i] + data.iloc[i + sample_size:]).index

    features_train = features.iloc[train_indexes]
    features_valid = features.iloc[valid_indexes]
    target_train = target.iloc[train_indexes]
    target_valid = target.iloc[valid_indexes]
    
    model = DecisionTreeClassifier(random_state=0)
    model = model.fit(features_train, target_train)
    score = model.score(target_valid, features_valid)
    
    scores.append(score)
 
final_score = score.mean()  
print('Средняя оценка качества модели:', final_score)

ValueError: Expected 2D array, got 1D array instead:
array=[0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1.
 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1.