NAME

       otbcli_TrainRegression - OTB TrainRegression application

DESCRIPTION

       This  is  the  TrainRegression application, version 5.2.0 Train a classifier from multiple
       images to perform regression.

       Complete documentation: http://www.orfeo-toolbox.org/Applications/TrainRegression.html

   Parameters:
       -progress
              <boolean>        Report progress

        -io.il                 <string list>    Input Image List  (mandatory)

       -io.csv
              <string>         Input CSV file  (optional, off by default)

       -io.imstat
              <string>         Input XML image statistics file  (optional, off by default)

        -io.out                <string>         Output regression model  (mandatory)

       -sample.mt
              <int32>          Maximum training predictors  (mandatory, default value is 1000)

       -sample.mv
              <int32>          Maximum validation predictors  (mandatory, default value is 1000)

       -sample.vtr
              <float>          Training and validation sample ratio  (mandatory, default value is
              0.5)

       -classifier
              <string>         Classifier to use for the training [dt/gbt/ann/rf/knn] (mandatory,
              default value is dt)

       -classifier.dt.max
              <int32>          Maximum depth of the tree  (mandatory, default value is 65535)

       -classifier.dt.min
              <int32>          Minimum number of samples in each node  (mandatory, default  value
              is 10)

       -classifier.dt.ra
              <float>           Termination  criteria  for  regression  tree  (mandatory, default
              value is 0.01)

       -classifier.dt.cat
              <int32>          Cluster possible values of a categorical variable into  K  <=  cat
              clusters to find a suboptimal split  (mandatory, default value is 10)

       -classifier.dt.f
              <int32>          K-fold cross-validations  (mandatory, default value is 10)

       -classifier.dt.r
              <boolean>        Set Use1seRule flag to false  (optional, off by default)

       -classifier.dt.t
              <boolean>        Set TruncatePrunedTree flag to false  (optional, off by default)

       -classifier.gbt.t
              <string>         Loss Function Type [sqr/abs/hub] (mandatory, default value is sqr)

       -classifier.gbt.w
              <int32>          Number of boosting algorithm iterations  (mandatory, default value
              is 200)

       -classifier.gbt.s
              <float>          Regularization parameter  (mandatory, default value is 0.01)

       -classifier.gbt.p
              <float>          Portion  of  the  whole  training  set  used  for  each  algorithm
              iteration  (mandatory, default value is 0.8)

       -classifier.gbt.max
              <int32>          Maximum depth of the tree  (mandatory, default value is 3)

       -classifier.ann.t
              <string>         Train Method Type [reg/back] (mandatory, default value is reg)

       -classifier.ann.sizes
              <string list>    Number of neurons in each intermediate layer  (mandatory)

       -classifier.ann.f
              <string>           Neuron  activation  function  type  [ident/sig/gau]  (mandatory,
              default value is sig)

       -classifier.ann.a
              <float>          Alpha parameter of the activation  function   (mandatory,  default
              value is 1)

       -classifier.ann.b
              <float>           Beta  parameter  of  the activation function  (mandatory, default
              value is 1)

       -classifier.ann.bpdw
              <float>          Strength of the  weight  gradient  term  in  the  BACKPROP  method
              (mandatory, default value is 0.1)

       -classifier.ann.bpms
              <float>           Strength  of the momentum term (the difference between weights on
              the 2 previous iterations)  (mandatory, default value is 0.1)

       -classifier.ann.rdw
              <float>          Initial value Delta_0 of update-values Delta_{ij} in RPROP  method
              (mandatory, default value is 0.1)

       -classifier.ann.rdwm
              <float>          Update-values lower limit Delta_{min} in RPROP method  (mandatory,
              default value is 1e-07)

       -classifier.ann.term
              <string>         Termination criteria [iter/eps/all] (mandatory, default  value  is
              all)

       -classifier.ann.eps
              <float>           Epsilon  value  used  in  the  Termination  criteria  (mandatory,
              default value is 0.01)

       -classifier.ann.iter
              <int32>          Maximum number of iterations  used  in  the  Termination  criteria
              (mandatory, default value is 1000)

       -classifier.rf.max
              <int32>          Maximum depth of the tree  (mandatory, default value is 5)

       -classifier.rf.min
              <int32>           Minimum number of samples in each node  (mandatory, default value
              is 10)

       -classifier.rf.ra
              <float>          Termination Criteria  for  regression  tree   (mandatory,  default
              value is 0)

       -classifier.rf.cat
              <int32>           Cluster  possible  values of a categorical variable into K <= cat
              clusters to find a suboptimal split  (mandatory, default value is 10)

       -classifier.rf.var
              <int32>          Size of the randomly selected subset of features at each tree node
              (mandatory, default value is 0)

       -classifier.rf.nbtrees <int32>
              Maximum number of trees in the forest  (mandatory, default value is 100)

       -classifier.rf.acc
              <float>           Sufficient  accuracy  (OOB  error)   (mandatory, default value is
              0.01)

       -classifier.knn.k
              <int32>          Number of Neighbors  (mandatory, default value is 32)

       -classifier.knn.rule
              <string>         Decision rule [mean/median] (mandatory, default value is mean)

       -rand  <int32>          set user defined seed  (optional, off by default)

       -inxml <string>         Load otb application from xml file  (optional, off by default)

EXAMPLES

       otbcli_TrainRegression -io.il training_dataset.tif -io.out regression_model.txt -io.imstat
       training_statistics.xml -classifier libsvm

SEE ALSO

       The  full  documentation for otbcli_TrainRegression is maintained as a Texinfo manual.  If
       the info and otbcli_TrainRegression programs are properly  installed  at  your  site,  the
       command

              info otbcli_TrainRegression

       should give you access to the complete manual.