matplotlib横plt.bar()竖plt.barh柱状图对比

最新推荐文章于 2025-03-01 12:53:28 发布

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本文展示了一种使用Python绘制随机森林特征重要性的方法，通过横纵柱状图直观地表现了糖尿病数据集中各特征与输出标签之间的相关系数。

# Show your work here - the plot below was helpful for me
# https://stackoverflow.com/questions/44101458/random-forest-feature-importance-chart-using-python
features = diabetes.columns[:diabetes.shape[1]]
print("features=",features)
importances = random_search.best_estimator_.feature_importances_
print("importances=",importances)
indicies = np.argsort(importances)
print("indicies=",indicies)
print("importances[indicies]=",importances[indicies])
plt.figure(1)
plt.barh(range(len(indicies)),importances[indicies],color='b',align='center')
plt.yticks(range(len(indicies)),features[indicies])
plt.xlabel("realative correlation")
plt.figure(2)
plt.bar(range(len(indicies)),importances[indicies])
plt.xticks(range(len(indicies)),features[indicies],rotation=45)

#效果

features= Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')
importances= [ 0.04620102  0.35829186  0.06762202  0.04479566  0.0593829   0.17193048
  0.10250037  0.14927569]
indicies= [3 0 4 2 6 7 5 1]
importances[indicies]= [ 0.04479566  0.04620102  0.0593829   0.06762202  0.10250037  0.14927569
  0.17193048  0.35829186]

下面两图的意义是各个特征值与输出标签的相关系数，用横纵柱状图进行描绘