随机森林分类做出精确度是100%

呃，一定是哪里错了。各位大侠有空帮忙看看，这是不太可能的结果啊。我把码贴下面了啊。

data_test_target = pd.read_csv(r"C:\Database\Titanic_Machine Learning from Disaster\gender_submission.csv")
data_test_m = pd.merge(data_test, data_test_target, how = 'left')
data_test_m.head(10)

out:

就是泰坦尼克那个数据集，对‘Embarked’, 'Sex’列的数据做了处理。数据集总样本数 = 418

predictors_test = ['Sex', 'Age', 'SibSp','Parch', 'Fare', 'Embarked','Pclass']

alg_R = RandomForestClassifier(n_estimators = 50, min_samples_split=4, min_samples_leaf=2, random_state=1)

kf = KFold(n_splits=8, random_state=1)

scores_Test = cross_val_score(alg_R, data_test_m[predictors_test], data_test_m['Survived'], cv=kf)

print(scores_Test)
print('-----------------')
print(scores_Test.mean())

图片为证啊，怎么跑，的出来的精确度结果都是100%。大神帮我看看。哪里出问题啦？？