---
name: "otbgui_TrainRegression"
section: "1"
platform: "linux"
package: "otb-bin-qt"
version: "6.6.1+dfsg-1+b1"
release: "Debian 10 (Buster)"
platforms: ["linux"]
url: "https://new.carta.tech/otbgui_TrainRegression"
---

# otbgui\_TrainRegression(1)

OTB TrainRegression application

## 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.