search for: classificator

Hello all, I am using the e1071 SVM with the tune options for classification, which work pretty well, given the examples of using tune.svm function for classification. But I have not found any example to tune the SVM novelty detection (one-classification) parameters (gamma, cost, nu), for example this are some of the options I have tried with no success: obj<-tune(svm, x,y, type

Dear all, I am having a hard time to figure out a suitable test for the match between two nominal classifications of the same set of data. I have used hierarchical clustering with multiple methods (ward, k-means,...) to classify my dat into a set number of classesa, and I would like to compare the resulting automated classification with the actual - objective benchmark one. So in principle I

Hello R-Users, I have been using (nnet) by Ripley to train a neural net on a test dataset, I have obtained predictions for a validtion dataset using: PP<-predict(nnetobject,validationdata) Using PP I can find the -2 log likelihood for the validation datset. However what I really want to know is how well my nueral net is doing at classifying my binary output variable. I am new to R and I

Hi all, I am doing binary classification and want to improve the classification results on imbalanced response data. Currently the performance is poor. Are there ways I could improve the performance? I could either try different classification methodologies, or try exploring the data more, and throwing away noisy data, and manipulate the data more before sending into the classifiers. I was

Hi everyone, Could somebody explain to me what is the package/function for classification analysis. I am performing analysis of music files in the form of MIDI files. I end up with about 750 dependent variables from the analysis, I also have a number of independent/grouping variables that I set manually. What I would like is to be able to predict which group a particular MIDI files belongs to

Hi All, I wrant to do Random Forest classification. I installed R, randomForest classifier package for R but dont know how to use it. Is there any Open Source Remote sensing application which do RF classification on satellite images? Anyone r has random forest classification example? Any language or package example no problem. Does anyone did it in R? if yes how? I google RF Classification

I have kindly asked for help and I am sad to receive such a reply from some on the r-help list. I did google it prior to sending my request, and I could not find any R package which provides GP classification model which produces prediction intervals for each sample. I would be grateful if anybody could inform me about it. Thank you. ________________________________ From: Bert Gunter

I was wandering if anybody ever tried to compare the classification accuracy of nnet to other (rpart, tree, bagging) models. From what I know, there is no reason to expect a significant difference in classification accuracy between these models, yet in my particular case I get about 10% error rate for tree, rpart and bagging model and 80% error rate for nnet, applied to the same data. Thanks.

Dear helpers, I have multiple vignette files for a package, and I would like to have the "right" order of these files when displayed online. For instance, see below: https://cran.r-project.org/web/packages/bst/index.html The order of vignette links on CRAN is different from what I hoped for: > vignette(package="bst") Vignettes in package 'bst': pros

Thank you Charles Berry for your kind reply. I don't see anything wrong with the word "struggling". I have spent several hours trying various R packages like kernlab and GPfit to use GP to create a binary classification model which produces a prediction interval for each sample. I have been struggling because with all of them you may create a GP classification model but it only

Hi what would you recommend to compare classification methods such as LDA, classification trees (rpart), bagging, SVM, etc: 10-fold cv (as in Ripley p. 346f) or leaving-one-out (as e.g. implemented in LDA)? my data-set is not that huge (roughly 200 entries) many thanks for a hint Christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

> On Dec 11, 2017, at 8:06 AM, Damjan Krstajic <dkrstajic at hotmail.com> wrote: > > I have kindly asked for help and I am sad to receive such a reply from some on the r-help list. > > Well, you only said you were `struggling' to find a package. Bert may well have done the Google search himself and found numerous resources on such models including links to R (as I

Hi R-list. This is my first post. I'll try to be as precise as possible with the difficulty I have to "get things done". I have a hard time trying to construct a double "for" loop and create within the inner loop new objects (in this case vectors). I posted this question in a non-directly related with pure R-problems list (in grass-stats). In addition, I think I

Dear all For my research, I am learning classification now. I was trying some example about classification tree pakages, such as tree and rpart, for instance, in Pima.te dataset have 8 variables (include class=type): library(rpart) library(datasets) pima.rpart <- rpart(type ~ npreg+glu+bp+skin+bmi+ped+age,data=Pima.te, method='class') plot(pima.rpart, uniform=TRUE) text(pima.rpart)

Hi there, I am interested in using R for machine learning (supervised classification). Currently, I have been investigating especially the rpart, tree, and randomForest package, and have achieved first results. are there any experiences, how the learned classificators could be used in e.g. C ? in other words, I want to "transfer" the learned predictor from R to C-code. for e.g. rpart, this could of course be done by hand, but, if the tree is sufficiently complex, this is very cumbersome. and for randomForest (which seems to work better for me), this...

I am implementing an image classification algorithm using the randomForest package. The training data consists of 31000+ training cases over 26 variables, plus one factor predictor variable (the training class). The main issue I am encountering is very low overall classification accuracy (a lot of confusion between classes). However, I know from other classifications (including a regular decision

Estimadísimo Carlos: Muchísimas gracias por responderme y hacerlo tan rápido. Contemplé esa posibilidad, es decir, que el hiperparámetro estuviera suponiendo un problema, y probé de esta forma: > learner <- lrn("classif.ranger", num.trees = 5, mtry = NULL) Error: Element with key 'classif.ranger' not found in DictionaryLearner!

So I have 2 sets of data - a training data set and a test data set. I've been doing the analysis on the training data set and then using predict and feeding the test data through that. There are 114 rows in the training data and 117 in the test data and 1024 columns in both. It's actually the same set of data split into two. The rows are made of 5 different numbers. They do represent

Hi, I'm experimenting with random forests and want to perform a binary classification task. I've tried some of the sample codes in the help files and things run, but I get a message to the effect 'you don't have very many unique values in the target - are you sure you want to do regression?' (sorry, don't know exact message but r is busy now so can't check). In

Dear All, I am struggling to find an R package which contains a function for building a Gaussian Process model for binary classification which may produce prediction intervals for predicted probabilities. I would be grateful if somebody could point me to such package. Thank you very much in advance. DK [[alternative HTML version deleted]]