{
 "comments" : {
	"-classifier":"             <string>         Classifier to use for the training [libsvm/boost/dt/gbt/ann/bayes/rf/knn] (mandatory, default value is libsvm)",
        "-classifier.libsvm.k":"    <string>         SVM Kernel Type [linear/rbf/poly/sigmoid] (mandatory, default value is linear)",
        "-classifier.libsvm.m":"    <string>         SVM Model Type [csvc/nusvc/oneclass] (mandatory, default value is csvc)",
        "-classifier.libsvm.c":"    <float>          Cost parameter C  (mandatory, default value is 1)",
        "-classifier.libsvm.opt":"  <boolean>        Parameters optimization  (optional, off by default)",
        "-classifier.libsvm.prob":" <boolean>        Probability estimation  (optional, off by default)",
        "-classifier.boost.t":"     <string>         Boost Type [discrete/real/logit/gentle] (mandatory, default value is real)",
        "-classifier.boost.w":"     <int32>          Weak count  (mandatory, default value is 100)",
        "-classifier.boost.r":"     <float>          Weight Trim Rate  (mandatory, default value is 0.95)",
        "-classifier.boost.m":"     <int32>          Maximum depth of the tree  (mandatory, default value is 1)",
        "-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.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)"


},

   	"svm_otb" : {
		"k":"linear",
		"m" : "csvc",
		"c" : "1",
		"opt" : "false",
		"prob" : "false",
		"rand" : "42"
	},

	"svm_scikit" : {
		"kernel":"linear",
		"C" : 1.0,
		"probability" : true
	},

   	"boost_otb" : {
		"t":"real",
		"w" : "100",
		"r" : "0.95",
		"m" : "1",
		"rand" : "42"
	},

   	"dt_otb" : {
		"max":"65535",
		"min" : "10",
		"ra" : "0.01",
		"cat" : "10",
		"f" : "5",
		"r" : "false",
		"t" : "false",
		"rand" : "42"
	},

   	"ann_otb" : {
		"t":"reg",
		"sizes" : "30",
		"f":"sig",
		"a" : "1",
		"b" : "1",
		"bpdw":"0.1",
		"bpms" : "0.1",
		"rdw":"0.1",
		"rdwm" : "1e-07",
		"term" : "all",
		"eps" : "0.01",
		"iter" : "1000",
		"rand" : "42"
	},

   	"rf_otb" : {
		"max":"25",
		"min" : "25",
		"ra":"0",
		"cat" : "10",
		"var" : "0",
		"nbtrees":"100",
		"acc" : "0.01",
		"rand" : "42"
	},

	"rf_scikit" : {
		"n_estimators":100,
		"max_depth" : 25,
		"min_samples_split":25,
		"random_state" : 42
	},

   	"knn_otb" : {
		"k":"32",
		"rand" : "42"
	},
	"ann_otb" : {
		"t":"reg",
		"sizes" : "30",
		"f":"sig",
		"a" : "1",
		"b" : "1",
		"bpdw":"0.1",
		"bpms" : "0.1",
		"rdw":"0.1",
		"rdwm" : "1e-07",
		"term" : "all",
		"eps" : "0.01",
		"iter" : "1000",
		"rand" : "42"
	},

	"ada_scikit" : {
		"n_estimators" : 100,
		"learning_rate" : 1.0,
		"random_state" : 42
	},

	"xtree_scikit" : {
		"n_estimators" : 100,
		"criterion" : "log_loss",
		"random_state" : 42
	},

   	"grad_scikit" : {
		"loss" : "log_loss",
		"n_estimators" : 100
	},
	"hist_scikit" : {
		"loss" : "log_loss",
		"max_iter" : 100
	}
}