From 8beef5f372ef8d3a78367096c2c976122e65cb93 Mon Sep 17 00:00:00 2001 From: "Anthony D. Blaom" Date: Mon, 3 Jun 2024 10:04:50 +1200 Subject: [PATCH] address review comment on docstring --- src/constructors.jl | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/constructors.jl b/src/constructors.jl index f3cbbd0..8ebffa1 100644 --- a/src/constructors.jl +++ b/src/constructors.jl @@ -104,14 +104,14 @@ Available controls: $CONTROLS_LIST. controls, refer to the documentation just cited. - `resampling=Holdout(fraction_train=0.7)`: The default resampling holds back 30% of data - for computing an out-of-sample estimate of performance (the "loss") for loss-based controls such - as `WithLossDo`. Specify `resampling=nothing` if all data is to - be used for controlled iteration, with each out-of-sample loss replaced by the most - recent training loss, assuming this is made available by the model - (`supports_training_losses(model) == true`). If the model does not report a - training loss, you can use `resampling=InSample()` instead, with an additional - performance cost. Otherwise, `resampling` must have type `Holdout` or be a vector with - one element of the form `(train_indices, test_indices)`. + for computing an out-of-sample estimate of performance (the "loss") for loss-based + controls such as `WithLossDo`. Specify `resampling=nothing` if all data is to be used + for controlled iteration, with each out-of-sample loss replaced by the most recent + training loss, assuming this is made available by the model + (`supports_training_losses(model) == true`). If the model does not report a training + loss, you can use `resampling=InSample()` instead. Otherwise, `resampling` must have + type `Holdout` or be a vector with one element of the form `(train_indices, + test_indices)`. - `measure=nothing`: StatisticalMeasures.jl compatible measure for estimating model performance (the "loss", but the orientation is immaterial - i.e., this could be a