搭配卡方特徵選取方法來找出有效提升分類器績效與訓練績效的特徵，並以決策

搭配卡方特徵選取方法來找出有效提升分類器績效與訓練績效的特徵，並以決策樹、隨機森林、XGBoost與KNN四種機器學習演算法進行分類，經測試組資料的測試後，以正確率與AUC值作為績效評估的指標。

結果 (英文) 1:

With chi-square feature selection methods to identify and effectively improve the performance of the classifier training performance characteristics,

and to decision trees, random forest, XGBoost and KNN four kinds of machine learning algorithms to classify test after test set of data in order to correct rate and AUC values as an indicator of performance evaluation.

結果 (英文) 2:

With the card feature selection method to find the characteristics that effectively improve the classifier performance and training performance,

The four machine learning algorithms of the determination tree, random forest, XGBoost and KNN were classified, and after testing the test group data, the correct rate and AUC value were used as the pointer to the performance evaluation.

結果 (英文) 3:

To find out the features that can effectively improve the performance of classifier and training performance by using the method of feature selection,
Four machine learning algorithms, decision tree, random forest, xgboost and KNN, are used to classify the algorithm. After testing the data of the test group, the accuracy and AUC value are used as the indicators of performance evaluation.