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

# otbcli\_TrainVectorClassifier(1)

OTB TrainVectorClassifier application

## NAME

otbcli\_TrainVectorClassifier - OTB TrainVectorClassifier application

## DESCRIPTION

This is the TrainVectorClassifier application, version 5.6.0 Train a classifier based on labeled geometries and a list of features to consider.

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

### Parameters:

- **-progress** \<boolean\>

  Report progress

 **-io.stats**              \<string\>         Input XML image statistics file  (optional, off by default)\
  **-io.confmatout**         \<string\>         Output confusion matrix  (optional, off by default)\
  **-io.out**                \<string\>         Output model  (mandatory)\
  **-feat**                  \<string list\>    Field names for training features.  (mandatory, default value is )\
  **-cfield**                \<string\>         Field containing the class id for supervision  (mandatory, default value is class)\
  **-layer**                 \<int32\>          Layer Index  (optional, on by default, default value is 0)\
  **-valid.vd**              \<string\>         Validation Vector Data  (optional, off by default)\
  **-valid.layer**           \<int32\>          Layer Index  (optional, on by default, default value is 0)\
  **-classifier**            \<string\>         Classifier to use for the training \[boost/dt/gbt/ann/bayes/rf/knn\] (mandatory, default value is boost)\
  **-classifier.boost.t**    \<string\>         Boost Type \[discrete/real/logit/gentle\] (mandatory, default value is real)\
  **-classifier.boost.w**    \<int32\>          Weak count  (mandatory, default value is 100)\
  **-classifier.boost.r**    \<float\>          Weight Trim Rate  (mandatory, default value is 0.95)\
  **-classifier.boost.m**    \<int32\>          Maximum depth of the tree  (mandatory, default value is 1)\
  **-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.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)\
  **-rand**                  \<int32\>          set user defined seed  (optional, off by default)\
  **-inxml**                 \<string\>         Load otb application from xml file  (optional, off by default)

## EXAMPLES

otbcli\_TrainVectorClassifier -io.vd vectorData.shp -io.stats meanVar.xml -io.out svmModel.svm -feat perimeter  area  width -cfield predicted