问题的提出：
- 如何使用梯度增强算法计算特征重要性。
- 如何在XGBoost模型计算的Python中绘制特征重要性。
- 如何使用XGBoost计算的要素重要性来执行要素选择。
使用梯度增强的好处是，在构建增强树之后，检索每个属性的重要性分数是相对简单的
手动绘制重要性
print(model.feature_importances_)
# plot 每一个特征的重要性，但是此处图中没有显示出特征的名称
pyplot.bar(range(len(model.feature_importances_)), model.feature_importances_)
pyplot.show()
利用自有库画图
# plot feature importance using built-in function
from numpy import loadtxt
from xgboost import XGBClassifier
from xgboost import plot_importance
from matplotlib import pyplot
# load data
dataset = loadtxt('pima-indians-diabetes.data.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
y = dataset[:,8]
# fit model no training data
model = XGBClassifier()
model.fit(X, y)
# plot feature importance
plot_importance(model)
pyplot.show()
特征筛选
# use feature importance for feature selection
from numpy import loadtxt
from numpy import sort
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.feature_selection import SelectFromModel
# load data
dataset = loadtxt('pima-indians-diabetes.data.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
Y = dataset[:,8]
# split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=7)
# fit model on all training data
model = XGBClassifier()
model.fit(X_train, y_train)
# make predictions for test data and evaluate
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
# Fit model using each importance as a threshold
# 返回特征重要性排序
thresholds = sort(model.feature_importances_)
print(thresholds)
for thresh in thresholds:
# select features using threshold，每次选择一个特征重要性分数，即排除一个特征
selection = SelectFromModel(model, threshold=thresh, prefit=True)
select_X_train = selection.transform(X_train)
# train model
selection_model = XGBClassifier()
selection_model.fit(select_X_train, y_train)
# eval model
select_X_test = selection.transform(X_test)
y_pred = selection_model.predict(select_X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print("Thresh=%.3f, n=%d, Accuracy: %.2f%%" % (thresh, select_X_train.shape[1], accuracy*100.0))
参考文献：
https://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python/