N2 <- yap::pnn.fit(X, Y, sigma = parm$best$sigma) Yap::logl(y_pred = predict(m1, newdata = X, type = "prob"), y_true = yap::dummies(Y)) # FIT A MULTINOMIAL REGRESSION AS A BENCHMARK # In this particular example, PNN even performed slightly better in terms of the cross-entropy for a separate testing dataset. As shown below, both approaches delivered very comparable predictive performance. Similar to GRNN, PNN shares same benefits of instantaneous training, simple structure, and global convergence.īelow is a demonstration showing how to use the YAP package and a comparison between the multinomial regression and the PNN. By the end of 2019, I finally managed to wrap up my third R package YAP ( ) that implements the Probabilistic Neural Network (Specht, 1990) for the N-category pattern recognition with N > 2.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |