Post-process KNN results across K to score consistency, (optionally) refine classified/Other cutoffs per-class and (optionally) assign composite classes
DLBCLone_ensemble_postprocess.RdPost-process KNN results across K to score consistency, (optionally) refine classified/Other cutoffs per-class and (optionally) assign composite classes
Usage
DLBCLone_ensemble_postprocess(
optimized_model,
assign_composites = FALSE,
other_min = 2,
any_split = TRUE,
min_purity = 0.75,
min_gap = 2,
optimize_per_class = TRUE
)Arguments
- optimized_model
Output of DLBCLone_optimize_params
- assign_composites
Logical; if TRUE, samples with split votes across multiple in-group classes will be assigned a composite class (e.g. "EZB/MCD") instead of "Other".
- other_min
Integer; For comparing across a range of K values,this is the threshold for the number of K values a sample is classified as Other for it to be re-assigned as Other. Set this to a high value if you don't want samples to be reassigned at all.
- any_split
Logical; if TRUE, any split among in-group votes across the Ks tested will triggers reassignment (or composite).
- min_purity
Numeric in the range of 0-1; top in-group vote share required to keep the top class instead of assigning a composite class.
- min_gap
Integer; top minus second in-group votes must be at least this gap to keep top class.
- optimize_per_class
Logical; If TRUE, a range of thresholds will be tested per class to optimize the classification/Other cutoff. This is a more complex approach that may yield better results but is not yet fully validated.