OTBCLI_BANDMATH(1)

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