TrainVectorClassifier-gbt otbcli_TrainVectorClassifier TrainVectorClassifier (gbt) Learning Train a classifier based on labeled geometries and a list of features to consider. ParameterVector io.vd Input Vector Data Input geometries used for training (note : all geometries from the layer will be used) False ParameterFile io.stats Input XML image statistics file XML file containing mean and variance of each feature. True OutputFile io.confmatout Output confusion matrix Output file containing the confusion matrix (.csv format). OutputFile io.out Output model Output file containing the model estimated (.txt format). ParameterString feat Field names for training features. List of field names in the input vector data to be used as features for training. False ParameterString cfield Field containing the class id for supervision Field containing the class id for supervision. Only geometries with this field available will be taken into account. class False ParameterNumber layer Layer Index Index of the layer to use in the input vector file. 0 True ParameterVector valid.vd Validation Vector Data Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) True ParameterNumber valid.layer Layer Index Index of the layer to use in the validation vector file. 0 True ParameterSelection classifier Classifier to use for the training Choice of the classifier to use for the training. gbt 0 False ParameterNumber classifier.gbt.w Number of boosting algorithm iterations Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. 200 False ParameterNumber classifier.gbt.s Regularization parameter Regularization parameter. 0.01 False ParameterNumber classifier.gbt.p Portion of the whole training set used for each algorithm iteration Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. 0.8 False ParameterNumber classifier.gbt.max Maximum depth of the tree The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. 3 False ParameterNumber rand set user defined seed Set specific seed. with integer value. 0 True