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
可知,8个或者5个特征就足够好了 ,odor、spore-print-color、population、gill-spacing、gill-size
后期更新……
后期更新……
- print('XGB_model.feature_importances_:','\n', XGB_model.feature_importances_)
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- from matplotlib import pyplot
- pyplot.bar(range(len(XGB_model.feature_importances_)), XGB_model.feature_importances_)
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- from xgboost import plot_importance
- plot_importance(XGB_model)
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- thresholds = sort(XGB_model.feature_importances_)
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- for thresh in thresholds:
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- selection = SelectFromModel(XGB_model, threshold=thresh, prefit=True)
- select_X_train = selection.transform(X_train)
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- selection_model = XGBClassifier()
- selection_model.fit(select_X_train, y_train)
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- 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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