決定木分析 - Qiita

# 参考サイト # https://smart-hint.com/ml/tree/ import seaborn as sns import pandas as pd from sklearn.model_selection import train_test_split from sklearn import tree df = sns.load_dataset('titanic') df.head() survived pclass sex age sibsp parch fare embarked class \ 0 0 3 male 22.0 1 0 7.2500 S Third 1 1 1 female 38.0 1 0 71.2833 C First 2 1 3 female 26.0 0 0 7.9250 S Third 3 1 1 female 35.0 1 0 53.10