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In full generality this is a very difficult question. The closest you will get to a general framework is Vapnik-Chervonenkis theory. You can read about this in Chapter 7.9 of "The elements of statistical learning" by Hastie, Tibshirani and Friedman which can be downloaded from their website . But be warned that this is a theoretical approach. Often more ...


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The following is a good way to judge the quality of fits for a model. http://en.wikipedia.org/wiki/Akaike_information_criterion


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You are confronted here in the common "bottom-up" or "top-down" problem. I think there is no final answer to your question, as both approach have their pros and cons. For the "bottom-up", you first classify for each feature, then classify again. This give you the ability to get a better understanding of the decision of your algorithm by splitting the ...


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Use all the attributes in a single model. If you build three separate models, you will be throwing away all all information that might be contained in combinations of different features. So, for example, it might be the case that prices are more likely to go up tomorrow if today's closing price was above the ema and volatility was high, but it is more ...


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The cross-validation procedure does not turn on the choice of algorithm. Yes - calculate the prediction error of the fitted models when predicting the V'th part of the data. Combine the V estimates of prediction average using a simple average. Subsets should be randomly sampled (roughly equally sized). 2a. Subsets should not overlap. No. As long as the ...



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