TrainRegression
Train a classifier from multiple images to perform regression.
Deprecated
QgsProcessingParameterMultipleLayers|io.il|Input Image List|3|None|False
QgsProcessingParameterFile|io.csv|Input CSV file|QgsProcessingParameterFile.File|None|None|True
QgsProcessingParameterFile|io.imstat|Input XML image statistics file|QgsProcessingParameterFile.File|None|None|True
QgsProcessingParameterFileDestination|io.out|Output regression model|None|None|False
QgsProcessingParameterNumber|sample.mt|Maximum training predictors|QgsProcessingParameterNumber.Integer|1000|True
QgsProcessingParameterNumber|sample.mv|Maximum validation predictors|QgsProcessingParameterNumber.Integer|1000|True
QgsProcessingParameterNumber|sample.vtr|Training and validation sample ratio|QgsProcessingParameterNumber.Double|0.5|True
OTBParameterChoice|classifier|Classifier to use for the training|dt;ann;rf;knn;sharkrf|0|True
QgsProcessingParameterNumber|classifier.dt.max|Maximum depth of the tree|QgsProcessingParameterNumber.Integer|10|True
QgsProcessingParameterNumber|classifier.dt.min|Minimum number of samples in each node|QgsProcessingParameterNumber.Integer|10|True
QgsProcessingParameterNumber|classifier.dt.ra|Termination criteria for regression tree|QgsProcessingParameterNumber.Double|0.01|True
QgsProcessingParameterNumber|classifier.dt.cat|Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split|QgsProcessingParameterNumber.Integer|10|True
QgsProcessingParameterBoolean|classifier.dt.r|Set Use1seRule flag to false|false|True
QgsProcessingParameterBoolean|classifier.dt.t|Set TruncatePrunedTree flag to false|false|True
OTBParameterChoice|classifier.ann.t|Train Method Type|back;reg|1|True
QgsProcessingParameterString|classifier.ann.sizes|Number of neurons in each intermediate layer|None|True|True
OTBParameterChoice|classifier.ann.f|Neuron activation function type|ident;sig;gau|1|True
QgsProcessingParameterNumber|classifier.ann.a|Alpha parameter of the activation function|QgsProcessingParameterNumber.Double|1|True
QgsProcessingParameterNumber|classifier.ann.b|Beta parameter of the activation function|QgsProcessingParameterNumber.Double|1|True
QgsProcessingParameterNumber|classifier.ann.bpdw|Strength of the weight gradient term in the BACKPROP method|QgsProcessingParameterNumber.Double|0.1|True
QgsProcessingParameterNumber|classifier.ann.bpms|Strength of the momentum term (the difference between weights on the 2 previous iterations)|QgsProcessingParameterNumber.Double|0.1|True
QgsProcessingParameterNumber|classifier.ann.rdw|Initial value Delta_0 of update-values Delta_{ij} in RPROP method|QgsProcessingParameterNumber.Double|0.1|True
QgsProcessingParameterNumber|classifier.ann.rdwm|Update-values lower limit Delta_{min} in RPROP method|QgsProcessingParameterNumber.Double|1e-07|True
OTBParameterChoice|classifier.ann.term|Termination criteria|iter;eps;all|2|True
QgsProcessingParameterNumber|classifier.ann.eps|Epsilon value used in the Termination criteria|QgsProcessingParameterNumber.Double|0.01|True
QgsProcessingParameterNumber|classifier.ann.iter|Maximum number of iterations used in the Termination criteria|QgsProcessingParameterNumber.Integer|1000|True
QgsProcessingParameterNumber|classifier.rf.max|Maximum depth of the tree|QgsProcessingParameterNumber.Integer|5|True
QgsProcessingParameterNumber|classifier.rf.min|Minimum number of samples in each node|QgsProcessingParameterNumber.Integer|10|True
QgsProcessingParameterNumber|classifier.rf.ra|Termination Criteria for regression tree|QgsProcessingParameterNumber.Double|0|True
QgsProcessingParameterNumber|classifier.rf.cat|Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split|QgsProcessingParameterNumber.Integer|10|True
QgsProcessingParameterNumber|classifier.rf.var|Size of the randomly selected subset of features at each tree node|QgsProcessingParameterNumber.Integer|0|True
QgsProcessingParameterNumber|classifier.rf.nbtrees|Maximum number of trees in the forest|QgsProcessingParameterNumber.Integer|100|True
QgsProcessingParameterNumber|classifier.rf.acc|Sufficient accuracy (OOB error)|QgsProcessingParameterNumber.Double|0.01|True
QgsProcessingParameterNumber|classifier.knn.k|Number of Neighbors|QgsProcessingParameterNumber.Integer|32|True
OTBParameterChoice|classifier.knn.rule|Decision rule|mean;median|0|True
QgsProcessingParameterNumber|classifier.sharkrf.nbtrees|Maximum number of trees in the forest|QgsProcessingParameterNumber.Integer|100|True
QgsProcessingParameterNumber|classifier.sharkrf.nodesize|Min size of the node for a split|QgsProcessingParameterNumber.Integer|25|True
QgsProcessingParameterNumber|classifier.sharkrf.mtry|Number of features tested at each node|QgsProcessingParameterNumber.Integer|0|True
QgsProcessingParameterNumber|classifier.sharkrf.oobr|Out of bound ratio|QgsProcessingParameterNumber.Double|0.66|True
QgsProcessingParameterNumber|rand|Random seed|QgsProcessingParameterNumber.Integer|0|True
