TrainRegression-gbt otbcli_TrainRegression TrainRegression (gbt) Learning Train a classifier from multiple images to perform regression. ParameterMultipleInput io.il Input Image List A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. False ParameterFile io.csv Input CSV file Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given True ParameterFile io.imstat Input XML image statistics file Input XML file containing the mean and the standard deviation of the input images. True OutputFile io.out Output regression model Output file containing the model estimated (.txt format). ParameterNumber io.mse Mean Square Error Mean square error computed with the validation predictors 0.0 False ParameterNumber sample.mt Maximum training predictors Maximum number of training predictors (default = 1000) (no limit = -1). 1000 False ParameterNumber sample.mv Maximum validation predictors Maximum number of validation predictors (default = 1000) (no limit = -1). 1000 False ParameterNumber sample.vtr Training and validation sample ratio Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). 0.5 False ParameterSelection classifier Classifier to use for the training Choice of the classifier to use for the training. gbt 0 False ParameterSelection classifier.gbt.t Loss Function Type Type of loss functionused for training. sqr abs hub 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