python xgboost输出变量重要性_XGBoost 输出特征重要性以及筛选特征

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))