TrainImagesClassifier-dt otbcli_TrainImagesClassifier TrainImagesClassifier (dt) Learning Train a classifier from multiple pairs of images and training vector data. ParameterMultipleInput io.il Input Image List A list of input images. False ParameterMultipleInput io.vd Input Vector Data List A list of vector data to select the training samples. False 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.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). ParameterNumber elev.default Default elevation This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. 0 ParameterNumber sample.mt Maximum training sample size per class Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. 1000 ParameterNumber sample.mv Maximum validation sample size per class Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. 1000 ParameterBoolean sample.edg On edge pixel inclusion Takes pixels on polygon edge into consideration when building training and validation samples. True 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 ParameterString sample.vfn Name of the discrimination field Name of the field used to discriminate class labels in the input vector data files. Class False ParameterSelection classifier Classifier to use for the training Choice of the classifier to use for the training. dt 0 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 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 ParameterNumber classifier.dt.ra Termination criteria for regression tree 0.01 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 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 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 ParameterBoolean classifier.dt.t Set TruncatePrunedTree flag to false If true, then pruned branches are physically removed from the tree. True ParameterNumber rand set user defined seed Set specific seed. with integer value. 0