标签：core UNC src orm bar fun tail tps 函数
1.输出XGBoost特征的重要性
from matplotlib import pyplot pyplot.bar(range(len(model_XGB.feature_importances_)), model_XGB.feature_importances_) pyplot.show()
from matplotlib import pyplot pyplot.bar(range(len(model_XGB.feature_importances_)), model_XGB.feature_importances_) pyplot.show()
也可以使用XGBoost内置的特征重要性绘图函数
# plot feature importance using built-in function from xgboost import plot_importance plot_importance(model_XGB) pyplot.show()
# plot feature importance using built-in function from xgboost import plot_importance plot_importance(model_XGB) pyplot.show()
2.根据特征重要性筛选特征
from numpy import sort
from sklearn.feature_selection import SelectFromModel
# Fit model using each importance as a threshold
thresholds = sort(model_XGB.feature_importances_)
for thresh in thresholds:
# select features using threshold
selection = SelectFromModel(model_XGB, 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))
from numpy import sort
from sklearn.feature_selection import SelectFromModel
# Fit model using each importance as a threshold
thresholds = sort(model_XGB.feature_importances_)
for thresh in thresholds:
# select features using threshold
selection = SelectFromModel(model_XGB, 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：https://blog.csdn.net/u011630575/article/details/79423162
标签：core UNC src orm bar fun tail tps 函数
原文地址：https://www.cnblogs.com/tan2810/p/11154630.html
