pub struct RiskSplitCounts {
pub judged: Option<i32>,
pub productive: Option<i32>,
pub rows: Option<i32>,
pub subjects: Option<i32>,
pub test: Option<i32>,
pub train: Option<i32>,
pub unproductive: Option<i32>,
pub val: Option<i32>,
}Fields§
§judged: Option<i32>Judged is how many rows carry a disposition. It is zero until a label plane writes one, and reporting it plainly is what lets a model plane refuse to rank rather than name a winner it cannot justify.
productive: Option<i32>Productive is how many judged rows carry the one disposition.
rows: Option<i32>Rows is how many rows the version holds across every split. It is the size of the version, not of the source window — the horizon, the cuts and the row cap all bind before this number.
subjects: Option<i32>Subjects is how many distinct subjects the rows belong to. Every row of one subject is in ONE split, so this is the real sample size — the row count flatters it whenever a subject is active.
test: Option<i32>Test is how many fall after the second cut — the LATEST slice, and the only one a score is honest about, since the split is temporal.
train: Option<i32>Train is how many rows fall before the first cut — the EARLIEST slice of the window, which is what a model is fitted on.
unproductive: Option<i32>Unproductive is how many carry the other. With Productive it accounts for Judged, so the class imbalance is visible before anyone trains on it; both stay 0 while Judged is 0.
val: Option<i32>Val is how many fall between the two cuts, held out for tuning.
Implementations§
Source§impl RiskSplitCounts
impl RiskSplitCounts
pub fn new() -> RiskSplitCounts
Trait Implementations§
Source§impl Clone for RiskSplitCounts
impl Clone for RiskSplitCounts
Source§fn clone(&self) -> RiskSplitCounts
fn clone(&self) -> RiskSplitCounts
1.0.0 (const: unstable) · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read more