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Predictive models are statistical models whose primary purpose is to predict other observations of a system optimally, as opposed to models whose purpose is to test a particular hypothesis or explain a phenomenon mechanistically. As such, predictive models place less emphasis on interpretability and more emphasis on performance.

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Had a general questions. Are there any good non-linear models with regularization? I've heard of some linear models with regularization but not too many non-linear ones. I understand that you can use …
asked Feb 29 '12 by tomas
9
votes
2answers
Just wanted to see if anyone has any experience applying Gaussian process regression (GPR) to high dimensional data sets. I'm looking into some of the various sparse GPR methods (e.g. sparse pseudo-in …
asked Jun 11 '12 by tomas
4
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2answers
Have a quick question about parameter selection for an SVM. I'm using a rbf kernel, so trying to optimize C and gamma. I have an example set of around 4500, about 700 features, and using 700 examples …
asked Feb 7 '12 by tomas
2
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1answer
Just a general question. Are there any good non-linear SVM (kernelized) implementations that include a regularization component (e.g. $L_1$, SCAD etc)? I've been looking around but man there are a lot …
asked Mar 2 '12 by tomas
2
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0answers
I've been reading/looking around for literature on support vector regressions that are relatively robust to outliers. I understand that standard SVRs can be significantly influenced by a few large out …
asked Apr 11 '12 by tomas
2
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1answer
Just a simple question on parameter selection for SVMs. If I use a minimum finding algorithm to find the optimal parameters for a set of data, how do I "average" the parameters over a set of cross val …
asked Feb 8 '12 by tomas
2
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Is there one? I think there's a penalizedSVM package in R but it looks to use a linear kernel. Can't quite tell from the documentation. If it's linear, is there a R package that lets me calculate the …
asked Mar 1 '12 by tomas
4
votes
1answer
I'm trying to use an inverse hyperbolic sine transformation to reduce the effect of outliers in my target variable. Unfortunately, I don't appear to have access to the basic papers on it. I've found t …
asked Apr 12 '12 by tomas
2
votes
1answer
I'm having some trouble getting the NM Simplex to find a good minimum for selecting hyperparameters of a rbf SVC. Not only am I tuning the 2 SVC parameters (C and gamma) I also have five class weights …
asked Mar 16 '12 by tomas
3
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2answers
Just want to check that I am performing my cross validation procedures right. I'm using a non-linear svm. I do a five fold cross validation (5 splits of test/train on my original training data) and fo …
asked Feb 9 '12 by tomas
2
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0answers
I saw a method for dimensionality reduction for the squared exponential covariance function (not ARD) whereby one uses a $G\times D$ projection matrix $P$ ($G < D$, $D$ = dimension of the inputs) such …
asked Jun 13 '12 by tomas
9
votes
1answer
I'm trying to build a prediction model with SVMs on fairly unbalanced data. My labels/output have three classes, positive, neutral and negative. I would say the positive example makes about 10 - 20% o …
asked Jan 11 '12 by tomas
12
votes
2answers
I have 2 general/more theoretical question. 1) I'm curious how SVMs handle variable interactions when building predictive models. E.g., if I have two features f1 and f2 and the target depends on f1, …
asked Jan 18 '12 by tomas
8
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3answers
I'm using support vector regression to model some fairly skewed data (with high kurtosis). I've tried modeling the data directly but I'm getting erroneous predictions I think mainly due to the distrib …
asked Apr 5 '12 by tomas
3
votes
2answers
I've been using elastic net implemented in R (via glmnet) for some modeling, but I was wondering, due to the number of outliers in my data, if there was some sort of modeling approach for regularized …
asked Sep 26 '12 by tomas

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