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| Machine Learning |

## Namespaces
| vigra::rf3
| Random forest version 3.
| vigra::rf::visitors
| Visitors to extract information during training of vigra::RandomForest version 2.

## Classes
class | DepthStop
| Random forest 'maximum depth' stop criterion. More...
class | EntropyScore
| Functor that computes the entropy score. More...
class | GiniScore
| Functor that computes the gini score. More...
class | KolmogorovSmirnovScore
| Functor that computes the Kolmogorov-Smirnov score. More...
class | NodeComplexityStop
| Random forest 'node complexity' stop criterion. More...
class | NumInstancesStop
| Random forest 'number of datapoints' stop criterion. More...
class | ProblemSpec< LabelType >
| problem specification class for the random forest. More...
class | PurityStop
| Random forest 'node purity' stop criterion. More...
class | RandomForest< LabelType, PreprocessorTag >
| Random forest version 2 (see also vigra::rf3::RandomForest for version 3) More...
class | RandomForestOptions
| Options object for the random forest. More...
class | Sampler< Random >
| Create random samples from a sequence of indices. More...
class | SamplerOptions
| Options object for the Sampler class. More...

## Detailed Description

This module provides classification algorithms that map features to labels or label probabilities. Look at the vigra::RandomForest class (for implementation version 2) or the vigra::rf3::random_forest() factory function (for implementation version 3) for an overview of the functionality as well as use cases.

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.11.1 (Fri May 19 2017)