search for: overfit

Dear listers, I'm using gam(from mgcv) for semi-parametric regression on small and noisy datasets(10 to 200 observations), and facing a problem of overfitting. According to the book(Simon N. Wood / Generalized Additive Models: An Introduction with R), it is suggested to avoid overfitting by inflating the effective degrees of freedom in GCV evaluation with increased "gamma" value(e.g. 1.4). But in my case, it didn't make a significant c...

I am analyzing particulate matter data (PM10) on a small data set (147 observations). I fitted a semi-parametric model and am worried about overfitting. How can one check for model fit in GAM? Jean G. Orelien

The subject is a Generalized Additive Model. Experts caution us against overfitting the data, which can cause inaccurate results. I am not a statistician (my background is in Computer Science). Perhaps some kind soul would take a look and vet the model for overfitting the data. The study estimated the ebb and flow of traffic through a voting place. Just one voting place wa...

...make it reproducible): (Full vignette: https://cran.r-project.org/web/packages/pbo/vignettes/pbo.html) library(pbo) #First, we assemble the trials into an NxT matrix where each column #represents a trial and each trial has the same length T. This example #is random data so the backtest should be overfit.` set.seed(765) n <- 100 t <- 2400 m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, dimnames=list(1:t,1:n)), check.names=FALSE) sr_base <- 0 mu_base <- sr_base/(252.0) sigma_base <- 1.00/(252.0)**0.5 for ( i in 1:n ) { m[,i] = m[,i] * sigma_base / sd(m[,...

I am accustomed to the LIBSVM package, which provides cross-validation on training with the -v option % svm-train -v 5 ... This does 5 fold cross validation while building the model and avoids over-fitting. But I don't see how to accomplish that in the e1071 package. (I learned that svm(... cross=5 ...) only _tests_ using cross-validation -- it doesn't affect the training.) Can

...dicted response [Y hat] on the horizontal axis). According to Frank Harrell's "Regression Modeling Strategies" book (pp. 61-63), when making such a plot on new data (having obtained a model from other data) we should expect the points to be around a line with slope < 1, indicating overfitting. As he writes, "Typically, low predictions will be too low and high predictions too high." However, when I make these plots, both with real data and with simple simulated data, I get the opposite: the points are scattered around a line with slope >1. Low predictions are too high a...

Hi all, I am using the "svm" command in the e1071 package. Does it have an automatic way of setting the "cost" parameter? I changed a few values for the "cost" parameter but I hope there is a systematic way of obtaining the best "cost" value. I noticed that there is a "cross" (Cross validation) parameter in the "svm" function. But I

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y ~ x1 + x2 + x3, x=TRUE, y=TRUE) corrected.Rsq <- rep(0,60) optimism.Rsq...

...l vignette: > https://cran.r-project.org/web/packages/pbo/vignettes/pbo.html) > > library(pbo) > #First, we assemble the trials into an NxT matrix where each column > #represents a trial and each trial has the same length T. This example > #is random data so the backtest should be overfit.` > > set.seed(765) > n <- 100 > t <- 2400 > m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, > dimnames=list(1:t,1:n)), check.names=FALSE) > > sr_base <- 0 > mu_base <- sr_base/(252.0) > sigma_base <- 1.00/(252.0)**0.5 > for...

On Fri, May 4, 2018 at 10:16 AM Sanjay Patel <spatel at rotateright.com> wrote: > I understand the overfit argument (but in most cases it just shows that a > unit test isn't minimized)... > Even minimized tests sometimes need a few other things to setup the circumstance (many DWARF tests, for example - produce the full DWARF output, but maybe you only care about one part of it (maybe you care...

Hi, I have a question about the parameter C (cost) in svm function in e1071. I thought larger C is prone to overfitting than smaller C, and hence leads to more support vectors. However, using the Wisconsin breast cancer example on the link: http://planatscher.net/svmtut/svmtut.html I found that the largest cost have fewest support vectors, which is contrary to what I think. please see the scripts below: Am I mis...

...make it reproducible): (Full vignette: https://cran.r-project.org/web/packages/pbo/vignettes/pbo.html) library(pbo) #First, we assemble the trials into an NxT matrix where each column #represents a trial and each trial has the same length T. This example #is random data so the backtest should be overfit.` set.seed(765) n <- 100 t <- 2400 m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, dimnames=list(1:t,1:n)), check.names=FALSE) sr_base <- 0 mu_base <- sr_base/(252.0) sigma_base <- 1.00/(252.0)**0.5 for ( i in 1:n ) { m[,i] = m[,i] * sigma_base / sd(m[,...

...thods that are available for selecting variables in a regression without simply imposing my own bias (having "good judgement"). The methods implimented in leaps and step and stepAIC seem to fall into the general class of stepwise procedures. But these are commonly condemmed for inducing overfitting. In Hastie, Tibshirani and Friedman "The Elements of Statistical Learning" chapter 3, they describe a number of procedures that seem better. The use of cross-validation in the training stage presumably helps guard against overfitting. They seem particularly favorable to shrinkage...

Hi there, Does anyone know how I solve for x from a given y in a polynomial function? Here's some example code: ##example file a<-1:10 b<-c(1,2,2.5,3,3.5,4,6,7,7.5,8) po.lm<-lm(a~b+I(b^2)+I(b^3)+I(b^4)); summary(po.lm) (please ignore that the model is severely overfit- that's not the point). Let's say I want to solve for the value b where a = 5.5. Any thoughts? I did come across the polynom package, but I don't think that does it- I suspect the answer is simpler than I am making it out to be. Any help would be welcome. -- Michael Rennie, Research...

I understand the overfit argument (but in most cases it just shows that a unit test isn't minimized)...but I don't see how the complete auto-generated assertions could be worse at detecting a miscompile than incomplete manually-generated assertions? The whole point of auto-generating complete checks is to catch mi...

Yep - all about balance. The main risk are tests that overfit (golden files being the worst case - checking that the entire output matches /exactly/ - this is what FileCheck is intended to help avoid) and maintainability. In the case of the autogenerated FileCheck lines I've seen so far - they seem like they still walk a fairly good line of checking exact...

...org/web/packages/pbo/vignettes/pbo.html) >>> >>> library(pbo) >>> #First, we assemble the trials into an NxT matrix where each column >>> #represents a trial and each trial has the same length T. This example >>> #is random data so the backtest should be overfit.` >>> >>> set.seed(765) >>> n <- 100 >>> t <- 2400 >>> m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, >>> dimnames=list(1:t,1:n)), check.names=FALSE) >>> >>> sr_base <- 0 >>> mu_bas...

...vignettes/pbo.html) >>>> >>>> library(pbo) >>>> #First, we assemble the trials into an NxT matrix where each column >>>> #represents a trial and each trial has the same length T. This example >>>> #is random data so the backtest should be overfit.` >>>> >>>> set.seed(765) >>>> n <- 100 >>>> t <- 2400 >>>> m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, >>>> dimnames=list(1:t,1:n)), check.names=FALSE) >>>> >>>> s...

On Fri, May 4, 2018 at 11:30 AM, David Blaikie <dblaikie at gmail.com> wrote: > > > On Fri, May 4, 2018 at 10:16 AM Sanjay Patel <spatel at rotateright.com> > wrote: > >> I understand the overfit argument (but in most cases it just shows that a >> unit test isn't minimized)... >> > > Even minimized tests sometimes need a few other things to setup the > circumstance (many DWARF tests, for example - produce the full DWARF > output, but maybe you only care about one...

...ps://cran.r-project.org/web/packages/pbo/vignettes/pbo.html) >> >> library(pbo) >> #First, we assemble the trials into an NxT matrix where each column >> #represents a trial and each trial has the same length T. This example >> #is random data so the backtest should be overfit.` >> >> set.seed(765) >> n <- 100 >> t <- 2400 >> m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, >> dimnames=list(1:t,1:n)), check.names=FALSE) >> >> sr_base <- 0 >> mu_base <- sr_base/(252.0) >> sig...