package orandforest
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orandforest
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package orandforest
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type c45_data = Oc45.FloatOc45.datatype c45_category = Oc45.FloatOc45.categorytype c45_trainSet = Oc45.FloatOc45.trainSetStores a random forest generated by genRandomForest
val genRandomForest : ?ncores:int -> int -> c45_trainSet -> randomForestgenRandomForest nbTrees trainSet generates a random forest of nbTrees trees, based on trainSet. If ncores > 1 is given, the random forest will be built in parallel
val classify : randomForest -> c45_data -> c45_categoryClassifies a new data vector, relying on the informations from the given random forest (generated by genRandomForest), yielding a category.
val classify_raw : randomForest -> c45_data -> (c45_category * float) listLike classify but gives access to the percentage of votes for each category. After calibration of your classifier (e.g. using Platt/sigmoid scaling or isotonic regression), those percentages can be converted to probabilities. No calibration method is included in the library currently.
val save_to_file : string -> randomForest -> unitsave_to_file output model saves the random forest model into the file output.
val restore_from_file : string -> randomForestrestore_from_file input restores the random forest that was previously saved in file input.