search for: overfitting

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 chang...

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 was st...

Hello, I'm trying to understand how to use the pbo package by looking at a vignette. I'm curious about a part of the vignette that creates simulated returns data. The package author transforms his simulated returns in a way that I'm unfamiliar with, and that I haven't been able to find an explanation for after searching around. I'm curious if I need to replicate the

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 and h...

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

...pentrace tells me that of the penalties 1, 2,..., 60, corrected AIC is maximised by a penalty of 9. This is consistent with the corrected R^2 plot, which shows a maximum somewhere around 10. However, a penalty of 9 still gives an R^2 optimism of 0.09 (training R^2=0.28, test R^2=0.19), suggesting overfitting. Do we just have to live with this R^2 optimism? It can be decreased by taking a bigger penalty, but then the corrected R^2 is reduced. Also, a penalty of 9 gives a corrected slope of about 1.17 (corrected slope of 1 is achieved with a penalty of about 1 or 2). Thanks for any help/advice you can...

Hi Joe, The centering and re-scaling is done for the purposes of his example, and also to be consistent with his definition of the sharpe function. In particular, note that the sharpe function has the rf (riskfree) parameter with a default value of .03/252 i.e. an ANNUAL 3% rate converted to a DAILY rate, expressed in decimal. That means that the other argument to this function, x, should be DAILY

...y names are rendered - but there's no option to produce only ranges without names (wouldn't really make sense)). > but I don't see how the complete auto-generated assertions could be worse > at detecting a miscompile than incomplete manually-generated assertions? > Generally overfitting wouldn't result in being bad at detecting failures, but excess false positives - if things in the output unrelated to the issue change (in intentional/benign ways) & cause the tests to fail often & become a burden for the project. Not suggesting that's the case with these particula...

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 misunde...

Wrong list. Post on r-sig-finance instead. Cheers, Bert On Nov 20, 2017 11:25 PM, "Joe O" <joerodonnell at gmail.com> wrote: Hello, I'm trying to understand how to use the pbo package by looking at a vignette. I'm curious about a part of the vignette that creates simulated returns data. The package author transforms his simulated returns in a way that I'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 thro...

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

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 miscompiles/regressions sooner. Ie, before they get committed and result in

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 exactly what's intended. Though I

Hi Eric, Thank you, that helps a lot. If I'm understanding correctly, if I?m wanting to use actual returns from backtests rather than simulated returns, I would need to make sure my risk-adjusted return measure, sharpe ratio in this case, matches up in scale with my returns (i.e. daily returns with daily sharpe, monthly with monthly, etc). And I wouldn?t need to transform returns like the

Correct Sent from my iPhone > On 21 Nov 2017, at 22:42, Joe O <joerodonnell at gmail.com> wrote: > > Hi Eric, > > Thank you, that helps a lot. If I'm understanding correctly, if I?m wanting to use actual returns from backtests rather than simulated returns, I would need to make sure my risk-adjusted return measure, sharpe ratio in this case, matches up in scale with

...39;s no option to produce only ranges without names (wouldn't really > make sense)). > > >> but I don't see how the complete auto-generated assertions could be worse >> at detecting a miscompile than incomplete manually-generated assertions? >> > > Generally overfitting wouldn't result in being bad at detecting failures, > but excess false positives - if things in the output unrelated to the issue > change (in intentional/benign ways) & cause the tests to fail often & > become a burden for the project. > > Not suggesting that's the c...

[re-sending - previous email went out by accident before complete] Hi Joe, The centering and re-scaling is done for the purposes of his example, and also to be consistent with his definition of the sharpe function. In particular, note that the sharpe function has the rf (riskfree) parameter with a default value of .03/252 i.e. an ANNUAL 3% rate converted to a DAILY rate, expressed in decimal. That