ML之xgboost：利用xgboost算法(自带特征重要性可视化+且作为阈值训练模型)训练mushroom蘑菇数据集(22+1,6513+1611)来预测蘑菇是否毒性(二分类预测)
目录
输出结果
设计思路
核心代码
输出结果
可知，8个或者5个特征就足够好了 ，odor、spore-print-color、population、gill-spacing、gill-size
设计思路后期更新……
后期更新……
print('XGB_model.feature_importances_：','\n', XGB_model.feature_importances_)
from matplotlib import pyplot
pyplot.bar(range(len(XGB_model.feature_importances_)), XGB_model.feature_importances_)
from xgboost import plot_importance
plot_importance(XGB_model)
thresholds = sort(XGB_model.feature_importances_)
for thresh in thresholds:
selection = SelectFromModel(XGB_model, threshold=thresh, prefit=True)
select_X_train = selection.transform(X_train)
selection_model = XGBClassifier()
selection_model.fit(select_X_train, y_train)
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))
