# 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