Per-endpoint class count table for a fitted CTA tree

Returns one row per terminal endpoint (leaf) per actual class, read directly from stored leaf node fields. No refitting, no prediction, and no recomputation from training data is performed.

Class counts are stored at fit time by oda_cta_fit on every terminal leaf. Row order within each endpoint follows the order of names(leaf$class_counts_raw), which is ascending by class label. Endpoints are ordered by node_id, matching cta_endpoint_summary.

Scope: This function exposes stored raw and weighted class counts only. It does not include target-class proportions, event rates, odds, or staging order. Staging-table and event-rate summaries are available via cta_staging_table.

If any terminal leaf is missing the stored class counts (i.e., the cta_tree was fitted by an earlier version of oda that did not store endpoint counts), the function stops with a clear error.

Usage

cta_endpoint_counts(tree)

Arguments

tree

A cta_tree from oda_cta_fit.

Value

A data.frame with one row per terminal endpoint per actual class and columns:

endpoint_id

Integer sequential endpoint index 1..n in node order, matching cta_endpoint_summary.

endpoint_node_id

Integer tree node identifier for this endpoint leaf.

path

Character; AND-joined branch labels from root to this leaf (e.g. "V14<=0.5 AND V15>0.5").

terminal_prediction

Integer class label assigned to this endpoint (stored leaf majority_class).

class

Character; actual class label for this row (e.g. "0", "1").

n_raw

Integer raw count of observations of this actual class reaching this endpoint.

n_weighted

Numeric weighted total for this actual class reaching this endpoint. Equals n_raw when case weights are not active.

For a no-tree fit the returned data frame has zero rows but the correct column structure and types.

See also

oda_cta_fit, cta_endpoint_summary, cta_confusion_table, cta_endpoint_table, cta_staging_table, cta_propensity_weights

Examples

data(mtcars)
X    <- mtcars[, c("cyl", "disp", "hp", "wt")]
y    <- as.integer(mtcars$am)
tree <- oda_cta_fit(X, y, mindenom = 5L, mc_iter = 500L, mc_seed = 42L)
cta_endpoint_counts(tree)
#>   endpoint_id endpoint_node_id     path terminal_prediction class n_raw
#> 1           1                2  wt>3.18                   1     0     2
#> 2           1                2  wt>3.18                   1     1    12
#> 3           2                3 wt<=3.18                   0     0    17
#> 4           2                3 wt<=3.18                   0     1     1
#>   n_weighted
#> 1          2
#> 2         12
#> 3         17
#> 4          1