OTBGUI_TRAINREGRESSION(1)

NAME
otbgui_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
otbgui_TrainRegression -io.il training_dataset.tif -io.out regression_model.txt -io.imstat training_statistics.xml -classifier libsvm

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

    info otbgui_TrainRegression

should give you access to the complete manual.