TrainRegression-rf otbcli_TrainRegression TrainRegression (rf) 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. rf 0 False ParameterNumber classifier.rf.max Maximum depth of the tree The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. 5 False ParameterNumber classifier.rf.min Minimum number of samples in each node If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. 10 False ParameterNumber classifier.rf.ra Termination Criteria for regression tree 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. 0 False ParameterNumber classifier.rf.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.rf.var Size of the randomly selected subset of features at each tree node The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. 0 False ParameterNumber classifier.rf.nbtrees Maximum number of trees in the forest The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. 100 False ParameterNumber classifier.rf.acc Sufficient accuracy (OOB error) Sufficient accuracy (OOB error). 0.01 False ParameterNumber rand set user defined seed Set specific seed. with integer value. 0 True