ランダムフォレストの特徴量の重要度

Coding

code: Python

import numpy as np

import pandas as pd

# wineデータセットを読み込む

df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)

df_wine.columns = 'Class label', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline'

# クラスラベルを表示

print('Class labels', np.unique(df_wine'Class label'))

df_wine.head()

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Class labels 1 2 3

Class label Alcohol Malic acid Ash Alcalinity of ash Magnesium Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue OD280/OD315 of diluted wines Proline

0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065

1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050

2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185

3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480

4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735

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code: Python

from sklearn.model_selection import train_test_split

# 特徴量とクラスラベルを別々に抽出

X, y = df_wine.iloc:, 1:.values, df_wine.iloc:, 0.values

# トレーニングデータとテストデータに分割

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, stratify=y)

%matplotlib inline

import matplotlib.pyplot as plt

from sklearn.ensemble import RandomForestClassifier

# Wineデータセットの特徴量の名称

feat_labels = df_wine.columns1:

# ランダムフォレストオブジェクトの生成（決定木の個数=500）

forest = RandomForestClassifier(n_estimators=500, random_state=1)

# モデルを適合

forest.fit(X_train, y_train)

# 特徴量の重要度を抽出

importances = forest.feature_importances_

# 重要度の降順で特徴量のインデックスを抽出

indices = np.argsort(importances)::-1

# 重要度の降順で特徴量の名称、重要度を表示

for f in range(X_train.shape1):

print('%2d) %-*s %f' % (f + 1, 30, feat_labels[indicesf], importances[indicesf]))

plt.title('Feature Importance')

plt.bar(range(X_train.shape1), importancesindices, align='center')

plt.xticks(range(X_train.shape1), feat_labelsindices, rotation=90)

plt.xlim([-1, X_train.shape1])

plt.tight_layout()

plt.show()

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1) Proline 0.185453

2) Flavanoids 0.174751

3) Color intensity 0.143920

4) OD280/OD315 of diluted wines 0.136162

5) Alcohol 0.118529

6) Hue 0.058739

7) Total phenols 0.050872

8) Magnesium 0.031357

9) Malic acid 0.025648

10) Proanthocyanins 0.025570

11) Alcalinity of ash 0.022366

12) Nonflavanoid phenols 0.013354

13) Ash 0.013279

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https://gyazo.com/930c290317d3c9c01450ac92ef89b368