See Javadoc for complete documentation of this class.
Usage: *No options*
These commands can be used to perform a variety of tasks, ranging from defining important settings about the object to actually using it.
clone – Create a copy of this model
nonlinear guess <name> <guess> – Set the initial guess for a certain parameter
name: Name of parameter to define guess for
guess: Initial guess for that parameter
nonlinear maxiter <maxiter> – Set maximum number of iterations for fitting routines
maxiter: Maximum number of iterations
normalize [attributes] [class] <method> [<options...>] – Define how to normalize data (data is not normalized by default)
attributes: Whether to normalize attributes
class: Whether to normalize class variable
method: Method used to normalize attributes
options...: Any options for the normalizer
output = crossvalidate $<dataset> <split size>> [<n repeats>] – Cross-validation by splitting dataset into train and test sets. Test is repeated multiple times
dataset: Dataset to use for cross validation
folds: Fraction of entries used in test set
n repeats: Number of times to repeat test
output: Dataset, result of used to compute performance statistics
Same command structure as k-fold cross-validation. Runs if the number of folds is less than 1.
output = crossvalidate $<dataset> [<folds>] – Use k-fold cross-validation to assess model performance.
dataset: Dataset to use for cross validation
folds: Number of cross validation folds (default = 10)
output: Dataset, result of used to compute performance statistics
Splits dataset into folds parts. Trains model on folds - 1 parts, validates against remaining part. Repeats using each part as the validation set.
robust <Q> – Define False Positive Rate used for robust regression. See Motulsky and Brown
Q: Desired FPR during outlier detection.
run $<dataset> – Use model to predict class values for each entry
dataset: Dataset to evaluate
set selector $<filter> – Define the BaseDatasetFilter used to filter data before attribute normalization, attribute selection, and model training.
filter: Filter to use
set selector $<selector> – Define the BaseAttributeSelector used to screen attributes before training
selector: Attribute selector to use
train $<dataset> – Train model using measured class values
dataset: Dataset used to train this model
validate $<dataset> – Validate model against external dataset
dataset: - Dataset to use for validate
These commands are run by calling "print <variable name> <command> [<options>]". Any output from that command will be printed to standard output.
description – Print out short description of this model.
model – Print out the model
selector – Print out attributes used selected by internal BaseAttributeSelector, if defined
training [<command>] – Print out statistics generated during training
command: Command to be passed to internal BaseStatistics object.
validation [<command>] – Print out statistics generated during validation
command: Command to be passed to internal BaseStatistics object.
Variables of this type can be saved in the following formats:
training – Print out performance data for training set
validation – Print out performance data for validation set