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Farm to Feed Shopping Basket Recommendation Challenge

Helping Kenya
€8 250 EUR
Completed (10 months ago)
Machine Learning
Prediction
Feature Engineering
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Starti
02 Dec 25
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19 Jan 26
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20 Jan 26
User avatar
Juliuss
Freelance
Rules
Help · 10 Dec 2025, 00:35 · 10

@AJoel and fellow scientists,

I’d like to clarify a point from the data section, which states:

“Any use of manually selected threshold (e.g., setting a fixed cut-off on probabilities) is strictly forbidden.”

My question is: If I use a standard classification model (e.g., LightGBM, XGBoost, Logistic Regression), these models naturally output probabilities, and the default decision rule is simply:

pred = (pred_proba >=0.5).astype(int)

So if I predict probabilities and then set the default 0.5 threshold is this also considered manually selected, and therefore forbidden?

Or is the intention only to forbid intentionally tuning thresholds (e.g., 0.42, 0.67) as part of feature engineering or validation?

Discussion 10 answers
User avatar
isaacOluwafemiOg
10 Dec 2025, 01:08

Hi Juliuss, I believe the thresholding example applies to the binary classification aspect of the challenge.

If that is the case, I don't think that rule really restricts us because auc calculation requires raw probability scores as opposed to label predictions that call for the kind of thresholding you referenced.

Upvotes 2
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Juliuss
10 Dec 2025, 08:38replied to isaacOluwafemiOg

Insightful. Thanks @isaacOluwafemiOg

Upvotes 3
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MICADEE
10 Dec 2025, 09:22replied to isaacOluwafemiOg

@isaacOluwafemiOg Yeah, that's very true "it only requires raw probability scores as opposed to label predictions".

Upvotes 4
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Juliuss
16 Dec 2025, 10:59replied to MICADEE

so rounding, clipping etc won't be accepted righ @MICADEE? @isaacOluwafemiOg?

Upvotes 0
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isaacOluwafemiOg
16 Dec 2025, 13:30replied to Juliuss

I wish I could give a definitive answer. I would rather rely on a response from @AJoel

Upvotes 1
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MICADEE
16 Dec 2025, 21:59replied to Juliuss

Sincerely, that's exactly what i did, I mean "raw probability scores" and I am also looking for clarification from @AJoel on this issue as well.

Upvotes 0
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AJoel
17 Dec 2025, 07:48replied to MICADEE

Hello @MICADEE, the outputs are raw probabilities. So the thresholding comments can be ignored since you are not returning predicted labels.

Upvotes 1
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Juliuss
17 Dec 2025, 10:02replied to AJoel

well currently my model would output negative quantities..lol...so i clip to 0. That's bad @AJoel? Quatities can go upto 2dp, 3dp, so i round to nearest one decimal place, that's bad too?

Upvotes 0
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AJoel
17 Dec 2025, 12:19replied to Juliuss

Obviously negative values don't make sense. So clipping is fine. I won't worry about values been rounded to the nearest one decimal place.

Upvotes 0
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Juliuss
17 Dec 2025, 12:50replied to AJoel

Thanks so much, this helps alot @AJoel🙏

Upvotes 0

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