fscore: f-score

In ateshkoul/PredPsych: Predictive Approaches in Psychology

Description

A simple function to generate F-scores (Fisher scores) for ranking features

Usage

fscore(Data, classCol, featureCol, silent = FALSE)

Arguments

Data	(dataframe) Data dataframe
classCol	(numeric) column with different classes
featureCol	(numeric) all the columns that contain features
silent	(optional) (logical) whether to print messages or not

Details

The function implements F-score for feature selection. F-score provides a measure of how well a single feature at a time can discriminate between different classes. The higher the F-score, the better the discriminatory power of that feature

The F-score is calculated for two classes

Value

named numeric f-scores

Author(s)

Atesh Koul, C'MON unit, Istituto Italiano di Tecnologia

[e-mail]

References

Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification. Wiley-Interscience (Vol. 24).

Chen, Y., & Lin, C.-J. (2006). Combining SVMs with Various Feature Selection Strategies. In I. Guyon, M. Nikravesh, S. Gunn, & L. A. Zadeh (Eds.), Feature Extraction: Foundations and Applications (Vol. 324, pp. 315-324). Berlin, Heidelberg: Springer Berlin Heidelberg.

Examples

# calculate f-scores for 10% of movement
fscore(KinData,classCol = 1,featureCol = c(2,12,22,32,42,52,62,72,82,92,102,112))
# Output:
# Performing Feature selection f-score analysis 
# --f-scores--

ateshkoul/PredPsych documentation built on Aug. 1, 2020, 5:27 p.m.