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)

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)

输出结果

后期更新……

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……

核心代码

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))
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