Calculates scores for assessing the relationship between individual features and the time-to-event outcome (right-censored survival data) using a univariate Cox proportional hazards model. The goal is to determine which features have a statistically significant association with the event of interest, typically in the context of clinical or biomedical research.
This filter fits a Cox Proportional Hazards model using
each feature independently and extracts the \(p\)-value that quantifies the
significance of the feature's impact on survival. The filter value is
-log10(p) where p is the \(p\)-value. This transformation is necessary
to ensure numerical stability for very small \(p\)-values. Also higher
values denote more important features. The filter works only for numeric
features so please ensure that factor variables are properly encoded, e.g.
using PipeOpEncode.
See also
PipeOpFilter for filter-based feature selection.
Other Filter:
Filter,
mlr_filters,
mlr_filters_anova,
mlr_filters_auc,
mlr_filters_boruta,
mlr_filters_carscore,
mlr_filters_carsurvscore,
mlr_filters_cmim,
mlr_filters_correlation,
mlr_filters_disr,
mlr_filters_find_correlation,
mlr_filters_importance,
mlr_filters_information_gain,
mlr_filters_jmi,
mlr_filters_jmim,
mlr_filters_kruskal_test,
mlr_filters_mim,
mlr_filters_mrmr,
mlr_filters_njmim,
mlr_filters_performance,
mlr_filters_permutation,
mlr_filters_relief,
mlr_filters_selected_features,
mlr_filters_variance
Super class
mlr3filters::Filter -> FilterUnivariateCox
Examples
filter = flt("univariate_cox")
filter
#> <FilterUnivariateCox:surv.univariate_cox>: Univariate Cox Survival Score
#> Task Types: surv
#> Properties: -
#> Task Properties: -
#> Packages: survival
#> Feature types: integer, numeric, logical