similar to: Creating new instances from original ones

Hola Comunidad, Feliz Año 2017: Tengo un problemilla con una regresión logística desbalanceada, tengo demasiados TRUE (93%). ¿Sabría alguién alguna forma de corregir el problema con R? Un slaudo, Milagros Camacho --- El software de antivirus Avast ha analizado este correo electrónico en busca de virus. https://www.avast.com/antivirus

Is there a built-in way to determine if a model object has just been created (ie. this object was the one that was originally persisted)? I can''t see it anywhere in the AR code, but thought I''d check. I want something like: p = Person.create p.original_instance? # true Is this built-in or should I just patch AR? -Jonathan.

--- Hod McWuff <hod@wuff.dhs.org> wrote: > On Fri, 2002-10-04 at 10:26, Marco "elcabesa" Belli wrote: > > oversampling.. i maean digitally change the wave file rate form > 44khz to 440 > > khz > > > > it make next sample easyer predictable > > OK, IANASPE (signal processing engineer) but it seems to me that if a > simple shift like that can

Mi matriz de datos inicial estaba muy desbalanceada (5% de la clase minoritaria), por lo que he creado con el algoritmo SMOTE un dataset balanceado con el que he creado el modelo, y luego sobre ese modelo he creado la matriz de confusión con los datos originales. Respecto a lo que me comentas, Carlos, creo que además de todo lo que comentas, que está bien, en micaso es necesario también saber no

some times ago i was playing with coding, shannon theoremes and other stuff, i have tried without success to compress audio wave, and i have notice that simply oversampling audio material enacnhe a lot compression ratio i only take awav file, oversampled it by 20 tiimes and then compressi it using pkzip or rar. i don0't remember if i also do a CONSTANT PREDICTION, iony know that pkzip and

constant prediction see this page http://flac.sourceforge.net/format.html oversampling.. i maean digitally change the wave file rate form 44khz to 440 khz it make next sample easyer predictable if i'll found my old work i'll tell you how much compression i could achieve

Hi:please i am working on fedora 31, then i installed R (language), and i want to install a R package ("DMwR"), but i get these warning messages ...: The downloaded source packages are in ?? ??/tmp/RtmpCBUyte/downloaded_packages? Updating HTML index of packages in '.Library' Making 'packages.html' ... done Warning messages: 1: In install.packages("DMwR") :

If no one has a better solution, split it, take a sample of size X from both and put it back together. hgwelec wrote: > > Dear members, > > Consider the following data frame (first 4 rows shown) > > > age sex class > 15 m low > 20 f high > 15 f low > 10 m low > > in my original data set i have 1200 rows and a class distribution

On Fri, Oct 04, 2002 at 01:57:03PM -0400, Hod McWuff wrote: > Agreed that the oversampling isn't useful in the long term. I'm not sure > what you mean by 'dictioniary overhead'. > > I'd like to see an easy-to-invoke set of parameters that will spare no > cpu expense and produce the tightest theoretically possible output. > > I'm guessing the best of

Hi. I am trying to install a package called DMwR http://cran.r-project.org/web/packages/DMwR/index.html located here: http://cran.r-project.org/bin/windows/contrib/r-release/DMwR_0.2.1.zip on windows 7. I am using R 2.10.1. I also tried typing something like this but it did not work well. install.packages(c(" http://cran.r-project.org/bin/windows/contrib/r-release/DMwR_0.2.1.zip

Hi. I was wondering if there was any interest in extending the flac container format to support sample rates above the 655k current limit. Please note this is not for using ridiculously high sample rate sourced audio for some imaginary audible benefit. I've been involved in some experimentation with offline upsampling in software prior to delivery to an external DAC as a way to bypass the

Hi all, I am trying to predict a target variable that takes values 0 or 1 using the rpart command. In my initial dataset I have few positive observations of the target variable; therefore I have oversampled the rare event by a multiple of 6 (i.e. from 762 to 4572). However, in my results, I end up with a number of positives in one of the terminal nodes that is not divisible by 6. As I have the

On Sat, Oct 05, 2002 at 12:26:12PM -0400, Hod McWuff wrote: > On Sat, 2002-10-05 at 03:19, Miroslav Lichvar wrote: > > On Fri, Oct 04, 2002 at 01:57:03PM -0400, Hod McWuff wrote: > > > Agreed that the oversampling isn't useful in the long term. I'm not sure > > > what you mean by 'dictioniary overhead'. > > > > > > I'd like to see

Agreed that the oversampling isn't useful in the long term. I'm not sure what you mean by 'dictioniary overhead'. I'd like to see an easy-to-invoke set of parameters that will spare no cpu expense and produce the tightest theoretically possible output. I'm guessing the best of Marco's idea can be achieved by adding heuristics to dynamically determine optimal frame

Hola, ¿cómo están? Soy estudiante de Ing. en Sistemas de Información y estoy cursando la materia Tecnologías para la Explotación de Datos. Me he decidido a usar R para hacer DM. Estoy recién comenzando con ambos. El asunto es que no he encontrado la implementación en R de uno de los algoritmos que dimos para árboles de decisión, ID3. Ya revisé varios de los paquetes para DM

On Mon, Sep 5, 2016 at 3:20 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > > > On Sun, Sep 4, 2016 at 1:06 PM, Simona Simona via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > >> Hello, >> >> I'm trying to add a new instruction after a given instruction in a basic >> block. >> Until LLVM 3.7, I was using the following code:

Why not just use Instruction::insertAfter()? I->insertAfter(new_inst); On Mon, Sep 5, 2016 at 8:13 AM, Ryan Taylor via llvm-dev < llvm-dev at lists.llvm.org> wrote: > Try incrementing the iterator before using. > > On Sep 5, 2016 10:26, "Simona Simona via llvm-dev" < > llvm-dev at lists.llvm.org> wrote: > >> On Mon, Sep 5, 2016 at 3:20 AM, Daniel

Hello, I'm trying to add a new instruction after a given instruction in a basic block. Until LLVM 3.7, I was using the following code: BB->getInstList().insertAfter(I, new_inst); [where both I and new_inst are Instruction*] In LLVM 3.8 however, the SymbolTableList was created as a wrapper over iplist. Could anyone please tell me how I can do the same type of insertion in LLVM 3.8?

Good afternoon. I would like to know if there is any function in R to do LOOCV with these classifiers: 1)SVM 2)Neural Networks 3)C4.5 ( J48) 4)KNN Thanks a lot! [[alternative HTML version deleted]]

some times ago i was playing with coding, shannon theoremes and other stuff, i have tried without success to compress audio wave, and i have notice that simply oversampling audio material enacnhe a lot compression ratio i only take awav file, oversampled it by 20 tiimes and then compressi it using pkzip or rar. i don0't remember if i also do a CONSTANT PREDICTION, iony know that pkzip