使用要素名称绘制要素重要性
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()
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