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)