特征输出重要性的排序

几个参考：
模型输出特征重要性排序
在 Python 中使用 XGBoost 的特征重要性和特征选择
关于seaborn作图
barplot&countplot&pointplot
Python Seaborn综合指南

1.用matplotlib

import pandas as pd
from xgboost import XGBClassifier
from xgboost import plot_importance
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from matplotlib import pyplot
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
dataset = pd.read_csv("C:\\Users\\Nihil\\Documents\\pythonlearn\\data\\pima-indians-diabetes.data.csv")

X = dataset.iloc[:,0:8]
y = dataset.iloc[:,8]

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=7)
model = XGBClassifier()
model.fit(X_train,y_train)
print(model.feature_importances_)
data = pd.DataFrame(model.feature_importances_)
data.columns = ['featureimportances']

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

2.用内置函数

plot_importance(model)
pyplot.show()