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spFSR

Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation

v2.0.4 · Mar 17, 2023 · GPL-3

Description

An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems).

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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies mlr3 future tictoc mlr3pipelines mlr3learners ranger (>= 0.14.1) ggplot2 lgr (>= 0.4.4) spFSR

Version History

6 tracked
new 2.0.4 Mar 10, 2026
updated 2.0.4 ← 2.0.3 diff Mar 16, 2023
updated 2.0.3 ← 2.0.2 diff Nov 12, 2022
updated 2.0.2 ← 2.0.1 diff Nov 7, 2022
updated 2.0.1 ← 1.0.0 diff Oct 28, 2022
new 1.0.0 May 10, 2018