Trying out a simple example using TuneSearchCV with LGBMClassifier and it fails on start.

Environment:

Python 3.8.3
tune-sklearn 0.3.0
ray 1.3.0
macos mojave 10.14.6

Code:

from ray.tune.sklearn import TuneSearchCV

from lightgbm import LGBMClassifier

lgmb_param_dists = dict(

                boosting_type=['gbdt','dart','rf'],
                num_leaves=(10,500),
 

We recently added some nightly jobs to our CI in #2250 !

The next step is set up a notification with the success or failure of the nightlies and a link to view the github pipeline.

I'm open to discussing what the medium for the notification should be but this looks interesting: https://github.com/marketplace/actions/slack-notify

We should add a description to each feature defined in smartcore in the Cargo.toml file, we could use as example the Cargo.toml file of the serde project.

We should add also the documentation for the "serde" feature that currently is not present in the Cargo.toml file.

Not sure if we can u

Is your feature request related to a problem? Please describe.
Feature is not directly related to a problem, but is rather an enhancement of existing functionality. As suggested by Julian King on the facet Slack channel, we could add Maximum Relevance Minimum Redundancy (MRMR) as a feature selection algorithm.

The algorithm is explained in the following papers:
https://arxiv.org/pdf/1

Is your feature request related to a problem? Please describe.
Currently the .fit method of GASearchCV accepts a callbacks argument, this is used to stop the training process when a condition is met. All the callbacks implements the same methods: .on_step and __call__ which are used while the optimization routine is running.
Currently, each callback does not have any kind of inheritance