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 unknown] [-t unknown] [-V bool] [-M unknown] [-c string] [-g string] [-E unknown] [-e unknown] [-i unknown] [-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) [unknown]
              A set of test points to estimate the density of.

       --training_file (-t) [unknown]
              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) [unknown]
              The output estimates on the test set from the final optimally pruned tree.

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

       --vi_file (-i) [unknown]
              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.