titanicの生存者データを使ってみる。
# 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 53.1000 S First
4 0 3 male 35.0 0 0 8.0500 S Third
who adult_male deck embark_town alive alone
0 man True NaN Southampton no False
1 woman False C Cherbourg yes False
2 woman False NaN Southampton yes True
3 woman False C Southampton yes False
4 man True NaN Southampton no True
df_x=df[["sex","pclass","fare"]]
df_y=df["survived"]
print(df_x,df_y)
sex pclass fare
0 male 3 7.2500
1 female 1 71.2833
2 female 3 7.9250
3 female 1 53.1000
4 male 3 8.0500
.. ... ... ...
886 male 2 13.0000
887 female 1 30.0000
888 female 3 23.4500
889 male 1 30.0000
890 male 3 7.7500
[891 rows x 3 columns] 0 0
1 1
2 1
3 1
4 0
..
886 0
887 1
888 0
889 1
890 0
Name: survived, Length: 891, dtype: int64
sex, pclass, fareを切り出したdfをdf_x、survivedを切り出したものをdf_yとしておく。df_xが被説明変数、df_yが説明変数。次に、質的データである性別をダミー変数を用いて0,1に直す。
df_x=df[["sex","pclass","fare"]]
df_x=pd.get_dummies(df_x,drop_first=True)
print(df_x)
df_x=df[["sex","pclass","fare"]]
df_x=pd.get_dummies(df_x,drop_first=False)
print(df_x)
df_x=df[["sex","pclass","fare"]]
df_x=pd.get_dummies(df_x,drop_first=False)
print(df_x)
pclass fare sex_male
0 3 7.2500 1
1 1 71.2833 0
2 3 7.9250 0
3 1 53.1000 0
4 3 8.0500 1
.. ... ... ...
886 2 13.0000 1
887 1 30.0000 0
888 3 23.4500 0
889 1 30.0000 1
890 3 7.7500 1
[891 rows x 3 columns]
pclass fare sex_female sex_male
0 3 7.2500 0 1
1 1 71.2833 1 0
2 3 7.9250 1 0
3 1 53.1000 1 0
4 3 8.0500 0 1
.. ... ... ... ...
886 2 13.0000 0 1
887 1 30.0000 1 0
888 3 23.4500 1 0
889 1 30.0000 0 1
890 3 7.7500 0 1
[891 rows x 4 columns]
pclass fare sex_female sex_male
0 3 7.2500 0 1
1 1 71.2833 1 0
2 3 7.9250 1 0
3 1 53.1000 1 0
4 3 8.0500 0 1
.. ... ... ... ...
886 2 13.0000 0 1
887 1 30.0000 1 0
888 3 23.4500 1 0
889 1 30.0000 0 1
890 3 7.7500 0 1
[891 rows x 4 columns]
drop_first=Trueにすることでmale=0の列を削除できて、male=1とできる。
# train_test_splitを使って学習用途テスト用にデータを分割
# random_state=1で乱数を固定
from sklearn.model_selection import train_test_split
train_x,test_x, train_y, test_y=train_test_split(df_x,df_y,random_state=1)
from sklearn import tree
model=tree.DecisionTreeClassifier(max_depth=2,random_state=1)
#モデルの作成
print(type(model))
#fit メソッドで学習
model.fit(train_x, train_y)
#モデルをpredictで予測
print(model.predict(test_x))
# 正解率の計算
model.score(test_x,test_y)
<class 'sklearn.tree._classes.DecisionTreeClassifier'>
[1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1
0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0
0]
0.7533632286995515
決定木の可視化を行う。
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
plt.figure(figsize=(15, 10))
plot_tree(model, feature_names=train_x.columns,
class_names=True,
filled=True)
plt.savefig("tree.png", facecolor="azure", edgecolor="coral")
plt.show()
デフォルトだと背景色が透明なので色をつけた。
sexのラベルが(male,female)=(1,0)。giniはジニ係数のことで後で詳しく調べる。value=[421,247]は、421人がグループA、247人がグループBということ。
class_name=Trueで目的変数のクラス名もグラフに追加できる。（y[0]だと死亡に振り分ける）
参考