compare_features {theftdlc} | R Documentation |
Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets
Description
Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets
Usage
compare_features(
data,
metric = c("accuracy", "precision", "recall", "f1"),
by_set = TRUE,
hypothesis = c("null", "pairwise"),
p_adj = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr")
)
Arguments
data |
list object containing the classification outputs produce by tsfeature_classifier
|
metric |
character denoting the classification performance metric to use in statistical testing. Can be one of "accuracy" , "precision" , "recall" , "f1" . Defaults to "accuracy"
|
by_set |
Boolean specifying whether you want to compare feature sets (if TRUE ) or individual features (if FALSE ). Defaults to TRUE but this is contingent on whether you computed by set or not in tsfeature_classifier
|
hypothesis |
character denoting whether p-values should be calculated for each feature set or feature (depending on by_set argument) individually relative to the null if use_null = TRUE in tsfeature_classifier through "null" , or whether pairwise comparisons between each set or feature should be conducted on main model fits only through "pairwise" . Defaults to "null"
|
p_adj |
character denoting the adjustment made to p-values for multiple comparisons. Should be a valid argument to stats::p.adjust . Defaults to "none" for no adjustment. "holm" is recommended as a starting point for adjustments
|
Value
data.frame
containing the results
Author(s)
Trent Henderson
References
Henderson, T., Bryant, A. G., and Fulcher, B. D. Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification. 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2023).
Examples
library(theft)
features <- theft::calculate_features(theft::simData,
group_var = "process",
feature_set = NULL,
features = list("mean" = mean, "sd" = sd))
classifiers <- classify(features,
by_set = FALSE,
n_resamples = 3)
compare_features(classifiers,
by_set = FALSE,
hypothesis = "pairwise")
[Package
theftdlc version 0.1.0
Index]