bionic (1) otbcli_TrainRegression.1.gz
NAME
otbcli_TrainRegression - OTB TrainRegression application
DESCRIPTION
This is the TrainRegression application, version 5.2.0 Train a classifier from multiple images to perform
regression.
Complete documentation: http://www.orfeo-toolbox.org/Applications/TrainRegression.html
Parameters:
-progress
<boolean> Report progress
-io.il <string list> Input Image List (mandatory)
-io.csv
<string> Input CSV file (optional, off by default)
-io.imstat
<string> Input XML image statistics file (optional, off by default)
-io.out <string> Output regression model (mandatory)
-sample.mt
<int32> Maximum training predictors (mandatory, default value is 1000)
-sample.mv
<int32> Maximum validation predictors (mandatory, default value is 1000)
-sample.vtr
<float> Training and validation sample ratio (mandatory, default value is 0.5)
-classifier
<string> Classifier to use for the training [dt/gbt/ann/rf/knn] (mandatory, default value
is dt)
-classifier.dt.max
<int32> Maximum depth of the tree (mandatory, default value is 65535)
-classifier.dt.min
<int32> Minimum number of samples in each node (mandatory, default value is 10)
-classifier.dt.ra
<float> Termination criteria for regression tree (mandatory, default value is 0.01)
-classifier.dt.cat
<int32> Cluster possible values of a categorical variable into K <= cat clusters to find
a suboptimal split (mandatory, default value is 10)
-classifier.dt.f
<int32> K-fold cross-validations (mandatory, default value is 10)
-classifier.dt.r
<boolean> Set Use1seRule flag to false (optional, off by default)
-classifier.dt.t
<boolean> Set TruncatePrunedTree flag to false (optional, off by default)
-classifier.gbt.t
<string> Loss Function Type [sqr/abs/hub] (mandatory, default value is sqr)
-classifier.gbt.w
<int32> Number of boosting algorithm iterations (mandatory, default value is 200)
-classifier.gbt.s
<float> Regularization parameter (mandatory, default value is 0.01)
-classifier.gbt.p
<float> Portion of the whole training set used for each algorithm iteration (mandatory,
default value is 0.8)
-classifier.gbt.max
<int32> Maximum depth of the tree (mandatory, default value is 3)
-classifier.ann.t
<string> Train Method Type [reg/back] (mandatory, default value is reg)
-classifier.ann.sizes
<string list> Number of neurons in each intermediate layer (mandatory)
-classifier.ann.f
<string> Neuron activation function type [ident/sig/gau] (mandatory, default value is sig)
-classifier.ann.a
<float> Alpha parameter of the activation function (mandatory, default value is 1)
-classifier.ann.b
<float> Beta parameter of the activation function (mandatory, default value is 1)
-classifier.ann.bpdw
<float> Strength of the weight gradient term in the BACKPROP method (mandatory, default
value is 0.1)
-classifier.ann.bpms
<float> Strength of the momentum term (the difference between weights on the 2 previous
iterations) (mandatory, default value is 0.1)
-classifier.ann.rdw
<float> Initial value Delta_0 of update-values Delta_{ij} in RPROP method (mandatory,
default value is 0.1)
-classifier.ann.rdwm
<float> Update-values lower limit Delta_{min} in RPROP method (mandatory, default value
is 1e-07)
-classifier.ann.term
<string> Termination criteria [iter/eps/all] (mandatory, default value is all)
-classifier.ann.eps
<float> Epsilon value used in the Termination criteria (mandatory, default value is
0.01)
-classifier.ann.iter
<int32> Maximum number of iterations used in the Termination criteria (mandatory,
default value is 1000)
-classifier.rf.max
<int32> Maximum depth of the tree (mandatory, default value is 5)
-classifier.rf.min
<int32> Minimum number of samples in each node (mandatory, default value is 10)
-classifier.rf.ra
<float> Termination Criteria for regression tree (mandatory, default value is 0)
-classifier.rf.cat
<int32> Cluster possible values of a categorical variable into K <= cat clusters to find
a suboptimal split (mandatory, default value is 10)
-classifier.rf.var
<int32> Size of the randomly selected subset of features at each tree node (mandatory,
default value is 0)
-classifier.rf.nbtrees <int32>
Maximum number of trees in the forest (mandatory, default value is 100)
-classifier.rf.acc
<float> Sufficient accuracy (OOB error) (mandatory, default value is 0.01)
-classifier.knn.k
<int32> Number of Neighbors (mandatory, default value is 32)
-classifier.knn.rule
<string> Decision rule [mean/median] (mandatory, default value is mean)
-rand <int32> set user defined seed (optional, off by default)
-inxml <string> Load otb application from xml file (optional, off by default)
EXAMPLES
otbcli_TrainRegression -io.il training_dataset.tif -io.out regression_model.txt -io.imstat
training_statistics.xml -classifier libsvm
SEE ALSO
The full documentation for otbcli_TrainRegression is maintained as a Texinfo manual. If the info and
otbcli_TrainRegression programs are properly installed at your site, the command
info otbcli_TrainRegression
should give you access to the complete manual.