NAME

       mlpack_det - density estimation with density estimation trees

SYNOPSIS

        mlpack_det [-f int] [-m unknown] [-L int] [-l int] [-p string] [-s bool] [-T string] [-t string] [-V bool] [-M unknown] [-c string] [-g string] [-E string] [-e string] [-i string] [-h -v]

DESCRIPTION

       This  program  performs  a  number  of functions related to Density Estimation Trees. The optimal Density
       Estimation Tree (DET) can be trained on a set of data (specified by '--training_file (-t)') using  cross-
       validation  (with  number  of  folds  specified  with the '--folds (-f)' parameter). This trained density
       estimation tree may then be saved with the '--output_model_file (-M)' output parameter.

       The variable importances (that is, the feature importance values for each dimension) may  be  saved  with
       the  '--vi_file  (-i)'  output  parameter, and the density estimates for each training point may be saved
       with the ’--training_set_estimates_file (-e)' output parameter.

       Enabling path printing for each node outputs the path from the root node to a leaf for each entry in  the
       test  set,  or  training  set  (if  a  test  set is not provided). Strings like 'LRLRLR' (indicating that
       traversal went to the left child, then the right child, then the  left  child,  and  so  forth)  will  be
       output. If 'lr-id' or 'id-lr' are given as the '--path_format (-p)' parameter, then the ID (tag) of every
       node along the path will be printed after or before the L or R  character  indicating  the  direction  of
       traversal, respectively.

       This  program  also can provide density estimates for a set of test points, specified in the '--test_file
       (-T)' parameter. The density estimation tree used for this task will be the tree that was trained on  the
       given  training points, or a tree given as the parameter '--input_model_file (-m)'. The density estimates
       for the test points may be saved using the ’--test_set_estimates_file (-E)' output parameter.

OPTIONAL INPUT OPTIONS

       --folds (-f) [int]
              The number of folds of cross-validation to perform for the estimation (0 is LOOCV)  Default  value
              10.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              Trained density estimation tree to load.

       --max_leaf_size (-L) [int]
              The maximum size of a leaf in the unpruned, fully grown DET. Default value 10.

       --min_leaf_size (-l) [int]
              The minimum size of a leaf in the unpruned, fully grown DET. Default value 5.

       --path_format (-p) [string]
              The format of path printing: 'lr', 'id-lr', or 'lr-id'. Default value 'lr'.

       --skip_pruning (-s) [bool]
              Whether to bypass the pruning process and output the unpruned tree only.

       --test_file (-T) [string]
              A set of test points to estimate the density of.

       --training_file (-t) [string]
              The data set on which to build a density estimation tree.

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_model_file (-M) [unknown]
              Output to save trained density estimation tree to.

       --tag_counters_file (-c) [string]
              The file to output the number of points that went to each leaf. Default value ''.

       --tag_file (-g) [string]
              The  file  to  output the tags (and possibly paths) for each sample in the test set. Default value
              ''.

       --test_set_estimates_file (-E) [string]
              The output estimates on the test set from the final optimally pruned tree.

       --training_set_estimates_file (-e) [string]
              The output density estimates on the training set from the final optimally pruned tree.

       --vi_file (-i) [string]
              The output variable importance values for each feature.

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  consult  the  documentation
       found at http://www.mlpack.org or included with your distribution of mlpack.