Parameterization
The file extension is ‘.prm’. All parameters must be given, even if they are not used. All parameters follow common tag-value notation. Rudimentary checks are performed by the software components using this file.
The ++PARAM_TRAIN_START++ and ++PARAM_TRAIN_END++ keywords enclose the parameter file.
The following parameter descriptions are a print-out of force-parameter, which can generate an empty parameter file skeleton.
++PARAM_TRAIN_START++
# INPUT
# ------------------------------------------------------------------------
# File that is holding the features for training (and probably validation).
# The file needs to be a table with features in columns, and samples in rows.
# Column delimiter is whitespace. The same number of features must be given
# for each sample. Do not include a header. The samples need to match the
# response file.
# Type: full file path
FILE_FEATURES = NULL
# File that is holding the response for training (class labels or numeric
# values). The file needs to be a table with one column, and samples in rows.
# Do not include a header. The samples need to match the feature file.
# Type: full file path
FILE_RESPONSE = NULL
# OUTPUT
# ------------------------------------------------------------------------
# File for storing the Machine Learning model in xml format. This file
# will be overwritten if it exists.
# Type: full file path
FILE_MODEL = NULL
# File for storing the logfile. This file will be overwritten if it exists.
# Type: full file path
FILE_LOG = NULL
# TRAINING
# ------------------------------------------------------------------------
# Response variable for training the model. This number refers to the column
# of the response file, in which the desired variable is stored (FILE_RESPONSE).
# Type: Integer. Valid range: [1,NUMBER_OF_VARIABLES]
RESPONSE_VARIABLE = 1
# This parameter specifies how many samples (in %) should be used for
# training the model. The other samples are left out, and used to vali-
# date the model.
# Type: Float. Valid range: ]0,100]
PERCENT_TRAIN = 70
# This parameter specifies whether the samples should be randomly drawn (TRUE)
# or if the first n samples (FALSE) should be used for training.
# Type: Logical. Valid values: {TRUE,FALSE}
RANDOM_SPLIT = TRUE
# Machine learning method. Currently implemented are Random Forest and
# Support Vector Machines, both in regression and classification flavors.
# Type: Character. Valid values: {SVR,SVC,RFR,RFC}
ML_METHOD = RFC
# Class weights. This parameter only applies for the classification flavor.
# This parameter lets you define à priori class weights, which can be useful
# if the training data are inbalanced. This parameter can be set to a number
# of different values. EQUALIZED gives the same weight to all classes (default).
# PROPORTIONAL gives a weight proportional to the class frequency.
# ANTIPROPORTIONAL gives a weight, which is inversely proportional to the class
# frequency. Alternatively, you can use custom weights, i.e. a vector of weights
# for each class in your response file. The weights must sum to one, and must be
# given in ascending order.
# Type: Character / Float list. Valid values: {EQUALIZED,PROPORTIONAL,ANTIPROPORTIONAL} or ]0,1[
FEATURE_WEIGHTS = EQUALIZED
# RANDOM FOREST PARAMETERS
# ------------------------------------------------------------------------
# This block only applies if method is Random Forest
# ------------------------------------------------------------------------
# Maximum number of trees in the forest. If RF_OOB_ACCURACY is 0, all trees
# will be grown. If RF_OOB_ACCURACY is set, the algorithm won't grow addi-
# tional trees if the accuracy is already met. If set to 0, additional trees
# are grown until RF_OOB_ACCURACY is met; note that this might never happen.
# RF_NTREE and RF_OOB_ACCURACY cannot both be 0.
# Type: Integer. Valid range: [0,...
RF_NTREE = 500
# Required accuracy of the ensemble, measured as OOB error. See also RF_NTREE.
# Type: Float. Valid range: [0,...
RF_OOB_ACCURACY = 0
# The number of randomly selected features at each tree node, which are used
# to find the best split. If set to 0, the square root of the number of all
# feature is used for the classification flavor; a third of the number of
# all feature is used for the regression flavor.
# Type: Integer. Valid range: [0,...
RF_NFEATURE = 0
# This parameter indicates whether the variable importance should be computed.
# Type: Logical. Valid values: {TRUE,FALSE}
RF_FEATURE_IMPORTANCE = TRUE
# If the number of samples in a node is less than this parameter then the
# node will not be split. If set to 0, it defaults to 1 for the classi-
# fication flavor; 5 for the regression flavor.
# Type: Integer. Valid range: [0,...
RF_DT_MINSAMPLE = 0
# The maximum possible depth of the tree. That is the training algorithms
# attempts to split a node while its depth is less than RF_DT_MAXDEPTH.
# The root node has zero depth. If set to 0, the maximum possible depth
# is used.
# Type: Integer. Valid range: [0,...
RF_DT_MAXDEPTH = 0
# Termination criteria for regression trees. If all absolute differences
# between an estimated value in a node and values of train samples in
# this node are less than this parameter then the node will not be
# split further.
# Type: Float. Valid range: [0.01,...
RF_DT_REG_ACCURACY = 0.01
# SUPPORT VECTOR MACHINE PARAMETERS
# ------------------------------------------------------------------------
# This block only applies if method is Support Vector Machine
# ------------------------------------------------------------------------
# Maximum number of iterations for the iterative SVM training procedure
# which solves a partial case of constrained quadratic optimization problem.
# If SVM_ACCURACY is 0, all iterations will be used. If SVM_ACCURACY is set,
# the algorithm will stop if the accuracy is already met. If set to 0, addi-
# tional iterations are computed until SVM_ACCURACY is met; note that this
# might never happen. SVM_MAXITER and SVM_ACCURACY cannot both be 0.
# Type: Integer. Valid range: [0,...
SVM_MAXITER = 1000000
# Required accuracy of the optimization. See also SVM_MAXITER.
# Type: Float. Valid range: [0,...
SVM_ACCURACY = 0.001
# Cross-validation parameter. The training set is divided into kFold sub-
# sets. One subset is used to test the model, the others form the train set.
# So, the SVM algorithm is executed kFold times.
# Type: Float. Valid range: [1,...
SVM_KFOLD = 10
# Parameter ϵ of a SVM optimization problem.
# Type: Float. Valid range: [0,...
SVM_P = 0
# Parameter C of a SVM optimization problem. This parameter expects three
# values which are used to perform a grid search, i.e. minimum value,
# maximum value, logarithmic step.
# Type: Float list. Valid range: [0,...
SVM_C_GRID = 0.001 10000 1
# Parameter γ of a kernel function. This parameter expects three values
# which are used to perform a grid search, i.e. minimum value, maximum
# value, logarithmic step.
# Type: Float list. Valid range: [0,...
SVM_GAMMA_GRID = 0.000010 10000 10
++PARAM_TRAIN_END++