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 = []
 
# зададим размер блока, если их всего три
sample_size = int(len(data)/3)
 
for i in range(0, len(data), sample_size):
    valid_indexes = data.loc[i: i+sample_size].index
    train_indexes = data[:i].index.union(data[i+sample_size:].index)
                # разбейте переменные features и target на выборки features_train, target_train, features_valid, target_valid
    # < напишите код здесь >
    features_valid = features.loc[valid_indexes]
    target_valid = target.loc[valid_indexes]
    features_train = features.loc[train_indexes]
    target_train = target.loc[train_indexes]
 
    model = DecisionTreeClassifier(random_state=0)
    model = model.fit(features_train, target_train)
    score = model.score(features_valid, target_valid) # < оцените качество модели >
   
    scores.append(score)
scores = pd.Series(scores)
final_score = scores.mean()
print('Средняя оценка качества модели:', final_score)