PredPsych (version 0.3)

fscore: f-score

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

Value

named numeric f-scores

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

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

# NOT RUN {
# 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--

# }