[Question] A “raw” version of the tiny_dataset.zip about srs-benchmark HOT 3 CLOSED

open-spaced-repetition/fsrs4anki#437

Keeping delta_t as floats:

1. Wouldn't improve scheduling in practice since Anki doesn't schedule cards (in the "review" phase) at a specific hour/minute of the day.
2. Wouldn't matter for long intervals. Rounding 1.5 to 2 introduces a large rounding error, rounding 365.5 to 366 introduces a very small rounding error.
3. Doesn't improve accuracy anyway, according to LMSherlock.

from srs-benchmark.

Ah, thank for the link! I didn't see that there was already a discussion on the topic.

I understand that the potential gains would be very small and there's a chance I'm overthinking this.
The main problem I want to solve is migrating a large collection of flashcards I created
in a spaced repetition system that's not Anki and where repetitions are scheduled using float intervals.
There I wonder if slightly better parameters computed earlier during the history of a card
would yield more accurate results during the lifetime of a card.

from srs-benchmark.

https://huggingface.co/datasets/open-spaced-repetition/fsrs-dataset/tree/main/float-delta-t

This dataset contain float delta_t.

from srs-benchmark.