search for: hyperparamet

Hi, i'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, I'm looking for a example of transformation of ARIMA in a state space equivalent to use the dlm package and calcualte the hyperparameters. Someone can help me about it?. If it's possible with a arima(1,0,1) example, or more complex model. While I have more examples best for me. Thanks all [[alternative HTML version deleted]]

Hello LIST, I'd like to use tune from e1071 to do a grid search for hyperparameter values in gbm. However, I can not get this to work. I note that there is no wrapper for gbm but that it is possible to use non-wrapped functions (like lm) without problem. Here's a snippet of code to illustrate. > data(mtcars) obj <- > gbm(mpg~disp+wt+carb,data=mtcars,distributi...

Hi, I believe that Caret uses a ?grid-serach approach. I was wondering if: 1 There are more efficient implementations for HP tuning for classification algos?(eg XGboost, CatBoost, SVM, RF etc),?using say?GM/SWARM approaches, akin to Google's approach AutoML for Image related Net problems? 2 This one is most probably wishful thinking, but is anyone looking at GM/SWARM at HP tuning across models

Hello again, A different but related question to my last one: Does anyone know if one can easily estimate state-space models using ML and the kalman filter using R? I would be especially interested in a relatively flexible function that would allow for estimation of hyperparameters, or could be made to do so. Thanks Michael J. Roberts Resource Economics Division, PMT USDA-ERS 202-654-5557 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "...

Dear users, I'm working on binary classification problem using Support Vector Machines (SVM). My objective is to train a series of SVM models on a grid of hyperparameters and then select those that maximize the AUC based on an independent validation sample. My attempted code is shown below. It runs well on "small" data sets but when I use it on a slightly larger sample (e.g., my train data is composed of about 8,000 observations on each class and 21 i...

...implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of columns. LargeVis is also insensitive to hyperparameter changes, which is important when running on large datasets that take time to compute. - Very fast approximate nearest neighbor search. I believe it to be the fastest nearest neighbor search available for R. - A fast implementation of the HDBSCAN clustering algorithm. HDBSCAN is a density-...

...implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of columns. LargeVis is also insensitive to hyperparameter changes, which is important when running on large datasets that take time to compute. - Very fast approximate nearest neighbor search. I believe it to be the fastest nearest neighbor search available for R. - A fast implementation of the HDBSCAN clustering algorithm. HDBSCAN is a density-...

...When I used commands "ecm.mix and dabipf.mix" to do a simulation (sample size is small 100), I got an error : Steps of ECM, missing value where True/False needed. I've checked the menu, and the option "prior" of ecm.mix said that if structural zeros appear in the table, hyperparameters for those cells should be set to NA. However, it didn't say how to do that in the command. I am wondering if someone knows how to fix this. I appreciate your help, Jia

...by the function ctable in library LearnBayes. (More precisely, a Bayes factor can be computed.) Two questions: 1) Is there any other package/function than can be used for this in a more or less straightforward manner? 2) The ctable help page explains the parameter a as "a: matrix of prior hyperparameters", but it is not explained what the prior is and how it precisely depends on these hyperparameters. (I read the paper test_of_independence.pdf by the package author J. Albert on the web, but it only explains the choice a=1+0*data, but not general a.) Thank you, Christian *** --- *** Chri...

...alternative ways to assess prediction accuracy? Or is option (A) only the accuracy of the svm when applied to the test set and therefore I should implement option (B) after I used option (A)? So would it be the correct way to use first (A) then do grid-search (via the tune command) to find the best hyperparameters and then test the trained svm by applying it to the test set? And in case I use a linear kernel instead of RBF, I guess I do not need to run grid-search as there are no hyperparameters to estimate? BEst, Jokel [[alternative HTML version deleted]]

...nel-based ranking method o onlearn() : Kernel-based Online Learning algorithms for classification, novelty detection and regression o kpca() : Kernel Pricipal Components Analysis o kcca() : Kernel Canonical Correlation Analysis o kfa() : Kernel Feature Analysis o sigest() : Hyperparameter estimation for the Gaussian and the Laplacian kernels o inchol() : Incomplete Cholesky decomposition method o csi() : Cholesky decomposition with side information o ipop() : Interior point-based Quadratic Optimizer Kernlab also includes a range of functions enabling the easy implementation...

...ian logistic regression models that consider the high-order interactions. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, we consider the full set of interaction patterns up to a specified order." The website of this package is http://fisher.utstat.toronto.edu/~longhai/software/BPHO/release.html -- Longhai Li, PhD Assistant Professor Departmen...

...nel-based ranking method o onlearn() : Kernel-based Online Learning algorithms for classification, novelty detection and regression o kpca() : Kernel Pricipal Components Analysis o kcca() : Kernel Canonical Correlation Analysis o kfa() : Kernel Feature Analysis o sigest() : Hyperparameter estimation for the Gaussian and the Laplacian kernels o inchol() : Incomplete Cholesky decomposition method o csi() : Cholesky decomposition with side information o ipop() : Interior point-based Quadratic Optimizer Kernlab also includes a range of functions enabling the easy implementation...

...#39;spam'),], type="one-svc",kernel="rbfdot",kpar=list(sigma=0.1),nu=0.05,cross=10) what I get is: > classifier Support Vector Machine object of class "ksvm" SV type: one-svc (novelty detection) parameter : nu = 0.05 Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.1 Number of Support Vectors : 660 Objective Function Value : 10.5781 Training error : 0.212907 Cross validation error : 0 --- What surprises me is that the Training error (which I suppose is the resubstitution error) is higher than the cross-validation error. Also, even changing...

...# build the model: seems successful > foo <- rvm(x, y) # same with ksvm(), gausspr(), ecc. Using automatic sigma estimation (sigest) for RBF or laplace kernel > foo Relevance Vector Machine object of class "rvm" Problem type: regression Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.00179432103430767 Number of Relevance Vectors : 7 Variance : 0.05937295 Training error : 0.049660537 # but predict fails... > predict(foo, x) Error in .local(object, ...) : test vector does not match model ! ******************************************************************...

...ian logistic regression models that consider the high-order interactions. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, we consider the full set of interaction patterns up to a specified order." The website of this package is http://fisher.utstat.toronto.edu/~longhai/software/BPHO/release.html -- Longhai Li, PhD Assistant Professor Departmen...

...for(i in 1:S){ Y[i,,] <- matrix(ungulate[i,],nrow=4,ncol=5,byrow=TRUE) } AY <- array(NA,dim=c((S+m),4,5))# Y of augmented for(i in 1:4){ y <- matrix(0,nrow=m,ncol=5) AY[,i,] <- rbind(Y[,i,],y) } ## bugs code library(R2OpenBUGS) sink("ungulate.txt") cat(" model{ # hyperparameters # habitat effects for each functional group for(i in 1:g){ # number of functional group for(j in 1:2){ # number of habitat type mu.h[i,j] ~ dnorm(0,0.0001) I(-2,2) # filling mu.h with values based on a random distribution...

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75

...er whenever I run it in openbugs it gives an error message saying: unknown type of logical function error pos 76. Any help would be appreciated. ## bugs code library(R2OpenBUGS) sink("C:/Users/CCF/Documents/Suzie Work/PTY Project/Waterhole Correction/ungulate.txt") cat(" model{ # hyperparameters # habitat effects for each functional group g <- length(table(G)) for(i in 1:g){ # number of functional group for(j in 1:3){ # number of habitat type mu.h[i,j] ~ dnorm(0,0.0001) I(-5,5) sigma.h[i,j] ~ dunif(0,5) tau.h[i,j] <- 1/(sigma.h[i,j]*sigma.h[i,j]) } } # detectability mu.r ~ d...

Hello, As far I saw in archive mailing list, I am not the first person with this problem. Anyway I was not able to pass this error once the information I got from the archive it is not very conclusive for this case. I have used linear, radial and sigmoid kernels for the same data in the same conditions and everything is ok. This problem just happens with the polynomial kernel. I send the