Hi David

I did try Ridge Regression as per my original message, but didn't get 
any results. Maybe I'm implementing it incorrecly.

Generally, the data set should work fine I think. I've correlated the 
features to the dependent variable and get high correlations for them 
independently. I'm not sure why the predictions are so off..

Zach


On 09/08/2012 18:20, David Warde-Farley wrote:
> On Thu, Aug 9, 2012 at 2:08 PM, Zach Bastick <[email protected]> wrote:
>
>> But as you can see, the predictions are absolutely terrible, no matter
>> what I do.
>> The training set predictions are quite accurate though. From my reading,
>> this could be due to over fitting. However, I don’t see how simple
>> linear model (OLS) could over fit anything…
> Quite easily. Overfitting is not really a property of the model family
> so much as how robustly you can estimate parameters given an amount of
> training data. Remember that the coefficients you are fitting specify a
> hyperplane in high dimensional space (where your geometric intuitions
> will often fail you), and unless your problem is truly linear and also
> noiseless, the OLS coefficients estimated on a finite training set are but
> an approximation of the "best" hyperplane for your problem (in terms of
> minimizing generalization error).
>
> With 8 features and only 35 cases, you're estimating 9
> parameters from very little data, and you will most likely need heavy
> regularization to avoid the model overspecializing to the quirks of
> the training set. I would try out ridge regression as a first pass,
> and maybe the Lasso.
>
> Cheers,
>
> David
>
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