matplotlib:使用特征名称绘制特征重要性 [英] matplotlib: Plot Feature Importance with feature names

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.但是在python中似乎缺少这种方法.我在 matplotlib 中搜索一个方法.

model.feature_importances 给我以下内容:

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])

然后使用以下绘图功能:

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

我得到一个条形图，但我想获得带有标签的条形图，同时重要性以排序方式水平显示.我也在探索 seaborn 并且找不到方法.

解决方案

不确定要查找的内容.来自

In R there are pre-built functions to plot feature importance of Random Forest model. But in python such method seems to be missing. I search for a method in matplotlib.

model.feature_importances gives me following:

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])

Then using following plotting function:

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

I get a barplot but I would like to get barplot with labels while importance showing horizontally in a sorted fashion. I am also exploring seaborn and was not able to find a method.

解决方案

Not exactly sure what you are looking for. Derived a example from here. As mentioned in the comment: you can change indices to a list of labels at line plt.yticks(range(X.shape[1]), indices) if you want to customize feature labels.

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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