Doing a PhD...

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Doing a PhD involves building walls and then demolish them and build them back learning something during the process.

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Data Streams and VFML

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We live in a technological world crowded of information. Every device we can think of can give us a bunch of such data, usually in the form of a flow or stream of information in, more or less, real time . In this particular situation classical knowledge discovery mechanisms (like our loved C4.5, a decision tree developed by Quinlan) are completely unable of extract a correct model of the situation. But, what is so special with flows of data? Following the words of Gama and Rodriques: a data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, following at high speed, and generated by non-stationary distributions in dynamic environments . So, to properly handle this kind of knowledge the learning algorithm has to learn on line and process massive amounts of data increasing the challenges to be faced. Let's hold one's breath w...
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Sometimes practitioners are forced to go beyond the standard methods in order to gain more accuracy with their models. If one analyzes the problem of rocketing accuracy, ensembling is a good starting point. However, the trick lies in getting enough generalization from feature space.  In this regard, ensemble generalization--do not confuse with classic or "standard" ensemble methods such as Random Forest or Gradient Boosting-- is the right path to follow, however complex. The idea is to combine predictions from "base learners" to train a second stage regressor, using these predictions as metafeatures. The trick is to use a J-fold cross-validation scheme and use always the same data partitions and seed. This kind of ensemble is often called stacking --as we "stack" layers of classifiers. Let’s do an example: suppose that we have three base learners: GBM, ET, and RF. Then assume we have a LM as level 2 learner. First we divide the training data into ...
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