Construct a new xplus object

Usage

new_xplus(
  fit_xplus = list(),
  pred_y = matrix(),
  cutoff = numeric(),
  predicted_coefficients = Matrix::Matrix(),
  n_iter = integer(),
  x = matrix(),
  y = numeric(),
  alpha = numeric(),
  learning_rate = numeric(),
  pseudo_labels = numeric(),
  iterative_path = character(),
  qq = numeric(),
  call = character(),
  max_iter = integer(),
  stop_reason = character(),
  original_y = NULL,
  final_labels = y,
  fallback_used = NULL,
  fallback_reason = NULL,
  history = NULL,
  sampling_counts = NULL,
  draw_counts = NULL,
  final_foldid = NULL,
  cv_measure = NULL,
  sigmoid_scale = NULL,
  sampling = NULL,
  min_iter = NULL,
  stability_window = NULL,
  min_coverage = NULL
)

Arguments

fit_xplus: Fitted glmnet::cv.glmnet() object.
pred_y: Predicted probabilities matrix.
cutoff: Numeric classification cutoff.
predicted_coefficients: Sparse coefficient matrix.
n_iter, history, sampling_counts, draw_counts: Number of completed iterations, per-iteration diagnostics, and per-observation unlabeled inclusion-round and draw counts; optional diagnostics default to NULL.
x: Training feature matrix used to fit the model.
y, final_labels: Identical actual target probabilities used for final fitting; final_labels defaults to y.
alpha: Elastic-net alpha used during fitting.
learning_rate: Learning rate used during pseudo-label updates.
pseudo_labels, original_y, fallback_used, fallback_reason: Proposed probabilities before fallback, original numeric binary labels, fallback flag and reason (empty if unused); optional metadata default to NULL for legacy bundles.
iterative_path, final_foldid, cv_measure, sigmoid_scale, sampling: Iterative path and optional final cross-validation folds, measure (deviance or auc), positive sigmoid scale, and sampling mode (bootstrap or unique); optional controls default to NULL.
qq: Quantile parameter used for cutoff calibration.
call: Original function call.
max_iter, min_iter, stability_window, min_coverage: Maximum iterations and optional minimum iterations, consecutive stability rounds, and unlabeled coverage required for stopping; optional controls default to NULL.
stop_reason: Reason fitting stopped: "max_iter", "label_stability", "budget_exhausted", or "degenerate_labels" (the pseudo-labels of the iterative training subset collapsed to a single class).

Value

An object of class "xplus".

Construct a new xplus object

Usage

Arguments

Value

See also