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As with many machine learning technologies, you can run a separate training and testing phase before deploying it live for prediction. All it does is build a collection of decision trees based on the parameters you give it - if the output field is a factor, you get classification (a finite enumerated set of values); if it's numeric, you get prediction. One ...


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I have not used random forests myself but I know of a guy who applied this classification technique to machine learning algorithms applied to pattern recognition. Thus I think its advantages over classic regression approaches can be applied to discern patterns in financial data, though I get the impression that it vastly overfits the data and thus you end ...


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A classifier can be weak for a number of reasons, and it mainly depends on characteristics of the data. For example, if the data are not linearly separable, then linear regression will be weak (poor correlation between predicted class and true class labels). However, if the data are linearly separable, then other classifiers may not work as well as linear ...


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It could help with things like fraud detection, analysis of bankruptcy probability, default risk, unsupervised learning for qualitative/descriptive purposes, or for a purely backwards looking supervised analysis on returns again for descriptive/understanding purposes (variable important, etc, perhaps impulse response analysis). It may also be good at ...


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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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