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).
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https://figshare.com/articles/dataset/_Evaluation_of_multiple_classification_models_including_Support_Vector_Machine_SVM_Random_Forest_RF_na_239_ve_Bayes_Bayes_Neural_Network_NNT_K_Nearest_Neighbor_KNN_and_Logistic_regression_models_via_10_fold_cross_validation_10FCV_/1058093
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资源简介：
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。
创建时间：
2014-06-16
