NAME

KEYWORDS

SYNOPSIS

Flags:

-w

Produce file containing weights at each step.

--verbose

Verbose module output

--quiet

Quiet module output

Parameters:

features=string

Input file containing the features (output of i.pr_features).

output=string

Name of the output file containing the selected features.

npc=integer

Number of the principal components to be used for the model development.
If not set all the principal components will be used.
Ignored if features does not contain principal components model.

svm_kernel=string

For svm: type of employed kernel.

Options: gaussian,linear

Default: linear

svm_kp=float

For svm: kernel parameter (Required parameter if you are using gaussian kernel).

svm_C=float

For svm: optimization parameter (Required parameter).

svm_cost=float

Cost parameter for the implementation of cost-sensitive procedure(w in [-1,1]).
w>0 results higher weight on examples of class 1.
w<0 results higher weight on examples of class -1.
w=0 corresponds to standard SVM.

Default: 0.0

fs_type=string

Feature selection method.

Options: rfe,e_rfe,1_rfe,sqrt_rfe

fs_rfe=integer

If you are using the e_rfe method, you have to choose the number of feartures
for the classical rfe method to start (fs_rfe>1).

svm_tol=float

For svm: tollerance parameter.

Default: 0.001

svm_eps=float

For svm: epsilon.

Default: 0.001

svm_maxloops=integer

For svm: maximum number of optimization steps.

Default: 1000

svm_verbose=integer

For svm: if it is set to 1 the number of loops will be printed.

Options: 0,1

Default: 0

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