Here is a list of different places in LightGBM's GitHub repo where we specify some dependencies or helpers. Quite often we should specify a particular version of such software. And these versions are tend to obsolete with time. If you see that there is a newer version comparing to what we have, please feel free to propose a PR with updates or simply leave a comment here.

• SHAs of commits of th

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

Is your feature request related to a problem? Please describe.
Currently, there are services that secure website from automation tools like ferret. Some of them send 405 in response to the DOCUMENT function call that make a ferret script fail with an error even though a page is available (not the original page, but usually a page with the captcha).

Describe the solution you'd like
It

The official instructions say to use joblib for pickling PyOD models.

This fails for AutoEncoders, or any other TensorFlow-backed model as far as I can tell. The error is:

>>> dump(model, 'model.joblib')
...
TypeError: can't pickle _thread.RLock objects

Note that it's not sufficient to save the underlying Keras S

Is your feature request related to a problem? Please describe.
I am wondering if there is a Random Grid Search equivalent of ForecastingGridSearchCV similar to how we have [RandomizedSearchCV](https://scikit-learn.org/stable/modul

When grouping by variable in Pivot Table, it would be nice if Group By would output an actual date for datetime variables.

E.g.:

• A mean of [2020-01-01, 2020-01-02, 2020-01-03] would output 2020-01-02.
• A median of [2020-01-01, 2020-01-02, 2020-01-03, 2020-01-03, 2020-01-04] would output 2020-01-03.
• A sum ... Don't know. Probably output a float?
• Min, max ... This one is obvious.
• Va