data-mining

Now that {lintr} 3.0 has been released and this project is using it (#5294), I believe the project should take advantage of additional linters added in that new version.

These are documented in "New and improved features" at https://cran.r-project.org/web/packages/lintr/news/news.html.

Reading through those new linters, I believe at least the following would be useful to add in LightGBM's

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Problem:

_catboost.pyx in _catboost._set_features_order_data_pd_data_frame()

_catboost.pyx in _catboost.get_cat_factor_bytes_representation()

CatBoostError: Invalid type for cat_feature[non-default value idx=1,feature_idx=336]=2.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.

Could you also print a feature name, not o

For the autoencoder in pyod, how do I adjust the learning rate?

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is.

Sktime repository contains a lot of notebooks (https://github.com/alan-turing-institute/sktime/tree/main/examples). The problem that motivated me to write this issue is mainly bad experience with notebook review process based on https://www.reviewnb.com/. To my taste it is

Unless I missed something, the documentation doesn't explain how to query document metadata (searching "site:montferret.dev metadata" through Google returned nothing, neither did grepping the source code).

As an example, I tried to query the og:url metadata.
I tried variations of //meta[property='og:url']::attr(content), with or without the leading //, and with or without the `attr(conte