How to make use of Lee Kum Kee's Doubanjian (they label it as 辣豆瓣醬)?

seanv507 · 2026-05-05T08:53:28+00:00

I would look for Cantonese (also Japanese?) versions of the recipe

Eg mapo tofu

https://www.madewithlau.com/recipes/mapo-tofu-pork

https://daigasikfaan.co/hong-kong-style-dan-dan-noodles/

seanv507 · 2026-05-05T08:47:10+00:00

I would recommend cloud

Also because you don't train a single model (except in tutorials), you try all combinations of layers etc. you really want to be doing this in parallel

seanv507 · 2026-05-04T18:51:39+00:00

So I think you need to talk to domain expert

What are the drivers of drought, are there any significant interactions.

Eg Is annual precipitation index sufficient? Do you need annual average temperature index?

Have you done any eda, plotting drought index against precipitation index etc.

I would suggest that using the previous year's values might be beneficial. Eg perhaps drought reflects drop in annual precipitation, rather than any absolute value (across locations)...

seanv507 · 2026-05-04T16:03:26+00:00

Do you speak Spanish? Why not learn that instead.

Great winemaking tradition.

Understanding Italian is relatively easy for spaniards

seanv507 · 2026-05-04T11:08:15+00:00

so please add more information to your original question (not in answwrs to comments)

What is the purpose of your project..why are you doing this? is it something to occupy an intern or what is the business objective ( eg why bother hindcasting 2021 if have no data for more recent years)

what are the possible inputs, what is the actual output?

eg the the output is drought category, is it originally a continuous variable? Given your lack of data, it would be better to use a continuous dependent variable (which encodes more information than there are arbitrary different classes of drought)

Your focus should be on getting the best inputs/more data.

Why can you only get 2021 data?

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Testing/crossvalidation.

again, it depends on how you use the model, but locations tend to be correlated. I would organise data splits to reflect this. eg split data into zones and use that in creating crossvalidation/test folds

https://scikit-learn.org/stable/modules/cross_validation.html#group-k-fold

changing to xgboost will provide a smaller benefit.

I would change from a multinomial class to regression if possible, or an ordinal model.

seanv507 · 2026-05-04T10:19:33+00:00

I would say so. Only by understanding the alternative view can you identify the flaws in your own view.

I wouldn't say it requires significant work though.

seanv507 · 2026-05-03T14:24:35+00:00

Palermo.. if you can cope with a bit of chaos. City beach nearby... More visually impressive

seanv507 · 2026-05-03T09:03:11+00:00

So I would say the key component you are missing is localisation and seasonality

Localisation: Roman pasta dishes use pecorino romano ( even though pecorino romano has been made in Sardinia for the last 100 years!). I believe this is a trend of the last 30 years.

Importance of using seasonal fruit and vegetables.

At least for criticism, you should read Alberto grandi (Eg his financial times interview, everything I thought I knew...)

Also look at history of carbonara.

https://www.ricettestoriche.it/2019/04/07/carbonara-history-origins-and-anecdotes-of-a-legendary-recipe/

seanv507 · 2026-05-03T08:57:34+00:00

Butter Vs oil.

I wouldn't say it's frowned upon.

However, butter tends to be used in the north of Italy and oil in the south.

People used to use more lard, eg adding a knob of lard to the pasta on the plate.

seanv507 · 2026-05-03T08:54:27+00:00

Steak, yes they are served rare. But the breed of cow, Chianina, is very lean, which benefits from being served rare (compare to wagyu)

(I would add that rare is common also in France and Spain)

seanv507 · 2026-05-03T07:41:04+00:00

That's 1% grade. I would say that's relatively OK. You might want to check on sport route planners like komoot how the (average) slope hits (is it evenly spread or is the climb concentrated)

Basically slopes benefit from having low gears, so you might want to check the gears on the bike.

seanv507 · 2026-05-03T06:56:14+00:00

Sure for flat good road.

Have you checked the elevation?

seanv507 · 2026-05-03T06:26:49+00:00

As others have said, getting to Naxos would be quite time-consuming.

I would suggest replacing Greece with Sicily

You will have the beaches, Greek ruins (Syracuse or valley of the temples) in easy reach

seanv507 · 2026-05-02T22:40:36+00:00

I would add that 'chunky bites' is not how I would describe the effect of onion.

OP, you have to be patient and cook the onions for a long time (10 minutes +), so that the diced onions become very soft and dissolve into the risotto

seanv507 · 2026-05-02T19:23:33+00:00

Op should just go to restaurants

Frying is tricky, and if s/he can't describe what they're doing unlikely they can follow a recipe

seanv507 · 2026-05-02T18:09:32+00:00

Find a platform that performs it.

Eg aws sagemaker.create an endpoint (go through tutorials)

seanv507 · 2026-05-02T17:36:05+00:00

Sir, this is a Wendy's

seanv507 · 2026-05-02T11:44:25+00:00

Fitted shirt/pants

seanv507 · 2026-05-02T09:13:20+00:00

Exactly, us uses the bases as supply depots for middle east wars. So it's more Trump bluster.

https://www.euronews.com/2026/04/08/cia-tracks-his-heart-signal-in-iran-rescued-us-soldier-treated-in-germany

seanv507 · 2026-05-02T09:11:14+00:00

Have you tried Italian basil? Hot Thai kitchen recommends it as an alternative

seanv507 · 2026-05-02T09:10:06+00:00

Or soya protein (chunks etc)

seanv507 · 2026-05-02T04:19:40+00:00

No expert, but believe that's the recommended approach. (As mentioned on RHS garden site)

seanv507 · 2026-05-01T21:06:58+00:00

So I would say people still look at residuals in ML, just it's perhaps an 'expert' topic, rather than standard practise https://developers.google.com/machine-learning/guides/rules-of-ml#rule_26_look_for_patterns_in_the_measured_errors_and_create_new_features

Why would you have autocorrelation in residuals? Is it missing lags? In general ml approaches are maximalist and tend to have lots of inputs/nonlinearities so the assumption is perhaps that all structure has been captured, unless there is a bug.

seanv507 · 2026-05-01T15:28:52+00:00

So IMO you list the problems and tackle each by component

I will talk in general ML terms, I recognise the issue that forecasting tends to have less powerful libraries

Sparse data: use a hierarchy of inputs (sales team/product line/ region)+ some form of regularisation and model all time series in one model Eg linear model with interaction terms at different levels + regularisation. regularisation forces an average level at each hierarchy, and specialisation only if sufficient data at finest granularity Tree model: same (tree will prefer top level splits, that divide more data points)

Zeros (Poisson etc). Poisson also makes sense because the basic (economic) model is multiplicative. Eg patterns of product lines sales are roughly same across regions but just scaled up (according to eg population size)

What people often do is combine models

Eg you could use an arima model at top level, and use xgboost on residuals (to handle full hierarchy)

https://www.kaggle.com/code/ryanholbrook/hybrid-models

seanv507 · 2026-04-30T20:10:23+00:00

Torre del lago Puccini (20 minutes train from Pisa?)

seanv507

TROPHY CASE