sklearn中xgboost模块中plot_importance函数（特征重要性）

# -*- coding: utf-8 -*-
"""
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# 作者：wanglei5205
# 邮箱：wanglei5205@126.com
# 代码：http://github.com/wanglei5205
# 博客：http://cnblogs.com/wanglei5205
# 目的：学习xgboost的plot_importance函数
# 官方API文档：http://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.training
###############################################################################
"""
### load module
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier
from xgboost import plot_importance

### load datasets
digits = datasets.load_digits()

### data analysis
print(digits.data.shape)
print(digits.target.shape)

### data split
x_train,x_test,y_train,y_test = train_test_split(digits.data,
                                                 digits.target,
                                                 test_size = 0.3,
                                                 random_state = 33)

model = XGBClassifier()
model.fit(x_train,y_train)

### plot feature importance
fig,ax = plt.subplots(figsize=(15,15))
plot_importance(model,
                height=0.5,
                ax=ax,
                max_num_features=64)
plt.show()

### make prediction for test data
y_pred = model.predict(x_test)

### model evaluate
accuracy = accuracy_score(y_test,y_pred)
print("accuarcy: %.2f%%" % (accuracy*100.0))
"""
95.0%
"""