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|   | vigra::rf::algorithms Namespace Reference |  | 
| Classes | |
| class | ClusterImportanceVisitor | 
| class | CorrectStatus | 
| class | Draw | 
| class | GetClusterVariables | 
| struct | HC_Entry | 
| class | HClustering | 
| class | NormalizeStatus | 
| class | PermuteCluster | 
| class | RFErrorCallback | 
| class | VariableSelectionResult | 
| Functions | |
| template<class FeatureT , class ResponseT , class ErrorRateCallBack > | |
| void | backward_elimination (FeatureT const &features, ResponseT const &response, VariableSelectionResult &result, ErrorRateCallBack errorcallback) | 
| template<class FeatureT , class ResponseT > | |
| void | cluster_permutation_importance (FeatureT const &features, ResponseT const &response, HClustering &linkage, MultiArray< 2, double > &distance) | 
| template<class FeatureT , class ResponseT , class ErrorRateCallBack > | |
| void | forward_selection (FeatureT const &features, ResponseT const &response, VariableSelectionResult &result, ErrorRateCallBack errorcallback) | 
| template<class FeatureT , class ResponseT , class ErrorRateCallBack > | |
| void | rank_selection (FeatureT const &features, ResponseT const &response, VariableSelectionResult &result, ErrorRateCallBack errorcallback) | 
This namespace contains all algorithms developed for feature selection
| void vigra::rf::algorithms::forward_selection | ( | FeatureT const & | features, | 
| ResponseT const & | response, | ||
| VariableSelectionResult & | result, | ||
| ErrorRateCallBack | errorcallback | ||
| ) | 
Perform forward selection
| features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm | 
| response | IN: n x 1 matrix containing the corresponding response | 
| result | IN/OUT: VariableSelectionResult struct which will contain the results of the algorithm. Features between result.selected.begin() and result.pivot will be left untouched. | 
| errorcallback | IN, OPTIONAL: Functor that returns the error rate given a set of features and labels. Default is the RandomForest OOB Error. | 
Forward selection subsequently chooses the next feature that decreases the Error rate most.
usage:
To use forward selection but ensure that a specific feature e.g. feature 5 is always included one would do the following
| void vigra::rf::algorithms::backward_elimination | ( | FeatureT const & | features, | 
| ResponseT const & | response, | ||
| VariableSelectionResult & | result, | ||
| ErrorRateCallBack | errorcallback | ||
| ) | 
Perform backward elimination
| features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm | 
| response | IN: n x 1 matrix containing the corresponding response | 
| result | IN/OUT: VariableSelectionResult struct which will contain the results of the algorithm. Features between result.pivot and result.selected.end() will be left untouched. | 
| errorcallback | IN, OPTIONAL: Functor that returns the error rate given a set of features and labels. Default is the RandomForest OOB Error. | 
Backward elimination subsequently eliminates features that have the least influence on the error rate
usage:
To use backward elimination but ensure that a specific feature e.g. feature 5 is always excluded one would do the following:
| void vigra::rf::algorithms::rank_selection | ( | FeatureT const & | features, | 
| ResponseT const & | response, | ||
| VariableSelectionResult & | result, | ||
| ErrorRateCallBack | errorcallback | ||
| ) | 
Perform rank selection using a predefined ranking
| features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm | 
| response | IN: n x 1 matrix containing the corresponding response | 
| result | IN/OUT: VariableSelectionResult struct which will contain the results of the algorithm. The struct should be initialized with the predefined ranking. | 
| errorcallback | IN, OPTIONAL: Functor that returns the error rate given a set of features and labels. Default is the RandomForest OOB Error. | 
Often some variable importance, score measure is used to create the ordering in which variables have to be selected. This method takes such a ranking and calculates the corresponding error rates.
usage:
| void vigra::rf::algorithms::cluster_permutation_importance | ( | FeatureT const & | features, | 
| ResponseT const & | response, | ||
| HClustering & | linkage, | ||
| MultiArray< 2, double > & | distance | ||
| ) | 
Perform hierarchical clustering of variables and assess importance of clusters
| features | IN: n x p matrix containing n instances with p attributes/features used in the variable selection algorithm | 
| response | IN: n x 1 matrix containing the corresponding response | 
| linkage | OUT: Hierarchical grouping of variables. | 
| distance | OUT: distance matrix used for creating the linkage | 
Performs Hierarchical clustering of variables. And calculates the permutation importance measures of each of the clusters. Use the Draw functor to create human readable output The cluster-permutation importance measure corresponds to the normal permutation importance measure with all columns corresponding to a cluster permuted. The importance measure for each cluster is stored as the status() field of each clusternode
usage:
| 
© Ullrich Köthe     (ullrich.koethe@iwr.uni-heidelberg.de)  | 
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