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(features_valid,target_valid)

   

scores.append(score)

 

 

final_score = sum(scores) / len(scores)

print('Средняя оценка качества модели:', final_score)