使用要素名称绘制要素重要性

2022-02-27 00:00:00 python matplotlib seaborn random-forest

问题描述

在R中有预先构建的函数来绘制随机森林模型的特征重要性。但在蟒蛇中,似乎缺少这种方法。我在matplotlib中搜索方法。

model.feature_importances提供以下信息:

array([  2.32421835e-03,   7.21472336e-04,   2.70491223e-03,
         3.34521084e-03,   4.19443238e-03,   1.50108737e-03,
         3.29160540e-03,   4.82320256e-01,   3.14117333e-03])

然后使用以下绘图函数:

>> pyplot.bar(range(len(model.feature_importances_)), model.feature_importances_)
>> pyplot.show()

我得到的是条形图,但我希望得到带有标签的条形图,同时以排序的方式水平显示重要性。我也在探索seaborn,但找不到方法。


解决方案

不能完全确定您要查找的内容。从here派生了一个示例。如评论中所述:如果要自定义要素标签,可以将indices更改为plt.yticks(range(X.shape[1]), indices)行的标签列表。

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
                           n_features=10,
                           n_informative=3,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
                              random_state=0)

forest.fit(X, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
             axis=0)
indices = np.argsort(importances)

# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.barh(range(X.shape[1]), importances[indices],
       color="r", xerr=std[indices], align="center")
# If you want to define your own labels,
# change indices to a list of labels on the following line.
plt.yticks(range(X.shape[1]), indices)
plt.ylim([-1, X.shape[1]])
plt.show()

相关文章