2014-04-28 18:56 GMT+02:00 Jacob Schreiber <[email protected]>: > I understand that HMMs do not perform classification in the same manner as > SVMs or Random Forest, but why is it not desirable to create a new section > to handle HMMs and possibly other graphical models? They seem like an > extremely useful and widespread part of machine learning, and I know from > personal experience that I'd prefer to have all my machine learning from the > same source if possible.
Because we try to provide a unified API for the basic tasks in machine learning, with pipelines and meta-algorithms like grid search to tie everything together. The required concepts, APIs, algorithms and expertise required for stuctured learning are different from what scikit-learn has to offer. If we started doing arbitrary structured learning, we'd need to redesign the whole package and the project would likely collapse under its own weight. That said, there are two project from scikit-learn contributors that do structured prediction: * pystruct [1] by Andreas, Vlad et al. handles general structured learning (focuses on SSVMs on arbitrary graph structures with approximate inference; defines the notion of sample as an instance of the graph structure) * seqlearn [2] by myself and others handles sequences only (focuses on exact inference; has HMMs, but mostly for the sake of completeness; treats a feature vector as a sample and uses an offset encoding for the dependencies between feature vectors) AFAIK, neither has solved the problem of putting a structured output learner at the end of a Pipeline, or putting one inside GridSearchCV. [1] http://pystruct.github.io/ [2] http://larsmans.github.io/seqlearn/ ------------------------------------------------------------------------------ "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE Instantly run your Selenium tests across 300+ browser/OS combos. Get unparalleled scalability from the best Selenium testing platform available. Simple to use. Nothing to install. Get started now for free." http://p.sf.net/sfu/SauceLabs _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
