Optimize all presets

I have always lacked the ability to make exceptions in the “Optimize all presets” function. I had the idea to enter such a search query so that it always finds 0 maps and thereby prevents the optimization of the preset I need. The question is, won’t I break something in this way?
Or maybe there is a better way to do this? For example, set ignoring reviews to an unrealistic date. For example, the year 2200.

The second question is not quite on the topic and not quite on FSRS. Is there any plans to support such a search query:
It would help to get weights based on the entire collection.
*That question goes away. I think a search query like that should work.

And then the third question. Are there any contraindications for using weight based on the entire collection instead of the standard ones?

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  1. There is a GitHub repo to investigate collection level vs deck level optimization.
    GitHub - open-spaced-repetition/fsrs-when-to-separate-presets

Maybe I didn’t express myself well. It meant the weights that would be used before the first full optimization.

  1. I’m not sure what you are trying to achieve. Why would you want the optimizer to have 0 reviews to work with?
  2. While I’ve never tried this, I believe using * works, yes.
  3. Are you asking whether using parameters that are based on the entire collection is better than using the default parameters? Most likely yes. You can verify by running “Evaluate” with the default parameters, and then with the new ones.
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I want some presets not to be optimized when using "Optimize all presets”.

I’m not sure if there is a way to do that. @L.M.Sherlock?

I don’t know why OP wants some presets not to be optimized when using "Optimize all presets”. And it would make Anki configuration complicated.

I think if it is done, it should be done through “Ignore reviews before”.

Regarding what you should do before a full optimisation, I had an idea about this. I don’t know how good parameters based on your own collection would be, but parameters based on a large collection of similar material must be better than the ones that come as default. I imagine it would be better if different learning communities had their own default parameters. So medical students collect data and have their parameters that you use before a full optimisation. And different language learning communities also have the same, etc., etc. ofc it would require a community effort to do that.

Yikes! No, no. Users shouldn’t be swapping around sets of optimized parameters. The defaults are the defaults for a reason and they are based on a suitably large data set. I think the depth of analysis FSRS has worked through and published has pretty clearly demonstrated that human brains are more alike than learning material is different.

I once proposed this to Sherlock. He only said you’d need to collect enough data. I personally think that we have very large communities online for most things that it’s not impossible to better the default parameters. Whether “human brains are more alike than learning material is different”, so much so that such an effort is not worthwhile, is something we won’t know. I think it requires only a little faith to do the experimentation first. If different learning materials have wildly different “default parameters” I’d then say the process has been worthwhile.

  1. Our 20k dataset doesn’t have text or media files, so we cannot classify cards into “med” or “language” or whatever. And we can’t ask Dae for a different dataset that has that info, because Anki doesn’t collect it.
  2. If there are multiple sets of default parameters, then how do you choose a set? Either Anki has to choose it for you, which would require adding another ML algorithm for classifiyng cards based on images/text PURELY for the sake of choosing a set of default parameters, or asking the user “What material do you have in this preset?” every single time when the user sets up another preset.

Overall, having multiple default presets would provide a small benefit at the cost of greatly increasing complexity and creating even more confusion. People are already confused by everything related to FSRS and many folks don’t even know what changing desired retention does. I’ve said this before, and I’ll say it again: FSRS is way past the Average Person Can Use It point, it currently lies somewhere in the Only For Experts abyss, it needs to be more user-friendly.

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I guess I’m an expert then lol /j

Actually I agree with everything you said. I am not really talking about default parameters. Therefore the “quotes” around it. I was thinking of an effort outside of the Anki community. Maybe if I can convince Ben and others from Refold to do it, or other people I know in that space, we can have “recommended parameters” optimised from the collection of tens of thousands of language learners. How many people would we need though? If the 20K dataset means 20K cards, I know plenty of people with that amount of cards in their collection (I have 30K) and then we’d need people. Large number of people. I just want “parameters” to be something part of the Anki meta. Not something that comes with Anki itself.

No, when I say 20k I mean “20 thousand collections”, and more than a billion reviews. I’m referring to this dataset:

This is what FSRS (and other algorithms) are benchmarked on: GitHub - open-spaced-repetition/srs-benchmark: A benchmark for spaced repetition schedulers/algorithms

Me and LMSherlock struggled to collect 70 collections using Google Forms. Trust me, collecting 20k collections without the help from Anki devs would be nigh impossible.


That’s sad :pensive:

I also had some other ideas. I was wondering whether people in different regions of the world learn things differently. I wouldn’t expect major differences but kids in Asia who grow up in a society that focuses on rote-learning might be a bit different from kids in the US. You can also compare teenagers with adults and see how different they are. But data seems to be the key. A key that’s hard to access.

Because I know that I will get bad parameters when optimizing. Or because I set up the parameters manually.

Example. I use this addon to replenish my vocabulary.
AnkiMorphs Introduction
There are 5-10 cards for each word in the short term and maybe up to 100 in the long term. Even if I use the “good” button all the time, I will learn the words.
So it turns out that I should either abandon optimization. Or honestly use the buttons, but then I will drown in the cards and slow down my learning. Or reduce the number of cards, which means not seeing the use of the word in different situations.
I chose not to optimize as the best option for me. This is just one example of why someone might need it.

I think you’ve fallen under the cognitive distortion of survivor error. Every day you answer the FSRS questions of those who could not figure it out, but you do not see all those who could.

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Maybe. Perhaps I should make a survey about people’s experience with setting FSRS up! The issue is that people on r/Anki and in the Anki discord server are obviously interested in, well, Anki. So they are more likely to put time and effort into learning how to use Anki properly. Still, a survey would be interesting. I’ll make one.

Anki has implemented a feature to keep the current parameters when the optimized parameters are worse.

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I think this add-on is incompatible with FSRS. I recommend creating a new profile to use it, or just turn off FSRS.

What’s your previous true retention and your current desired retention?