TrainRegression-dt otbcli_TrainRegression TrainRegression (dt) 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. dt 0 False ParameterNumber classifier.dt.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. 65535 False ParameterNumber classifier.dt.min Minimum number of samples in each node If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. 10 False ParameterNumber classifier.dt.ra Termination criteria for regression tree 0.01 False ParameterNumber classifier.dt.cat Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. 10 False ParameterNumber classifier.dt.f K-fold cross-validations If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. 10 False ParameterBoolean classifier.dt.r Set Use1seRule flag to false If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. True True ParameterBoolean classifier.dt.t Set TruncatePrunedTree flag to false If true, then pruned branches are physically removed from the tree. True True ParameterNumber rand set user defined seed Set specific seed. with integer value. 0 True