Evaluation of multiple classification models including Support Vector Machine (SVM), Random Forest (RF), naïve Bayes (Bayes), Neural Network (NNT), K-Nearest Neighbor (KNN) and Logistic regression models via 10-fold cross-validation (10FCV).

Evaluation of multiple classification models including Support Vector Machine (SVM), Random Forest (RF), naïve Bayes (Bayes), Neural Network (NNT), K-Nearest Neighbor (KNN) and Logistic regression models via 10-fold cross-validation (10FCV).本研究通过10折交叉验证（10FCV）对多种分类模型开展性能评估，所涉及的模型包括支持向量机（Support Vector Machine，SVM）、随机森林（Random Forest，RF）、朴素贝叶斯（naïve Bayes，Bayes）、神经网络（Neural Network，NNT）、K近邻（K-Nearest Neighbor，KNN）以及逻辑回归模型。