model-selection

What happened + What you expected to happen

The shim tune.create_scheduler() does not properly parse the keyword parameters passed in a dictionary for the pb2 scheduler. For this call

pb2_parm_dict = {"time_attr": "time_total_s", "metric": "metric_score", "mode": "min",
                 "hyperparam_bounds": {"param1": [0, 1]}}

pb2_scheduler = create_scheduler("pb2", **pb2_pa

I am unable to install EvalMl on an M1 Mac in an arm64 terminal via pip or conda. As M1 Macs become more widely used, the install instructions for EvalML might need to provide some guidance on what to do.

pip install evalml - doesn't work
conda install -c conda-forge evalml - doesn't work
conda install -c conda-forge evalml-core - does work

Note: @dvreed77 was able to install EvalML

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.
NA

Describe the solution you'd like
Implement in the sklearn_genetic.plots module a function named plot_parallel_coordinates to inspect the results of the learning process

Describe alternatives you've considered
The function should take two arguments:

• estimator: A fitted estimator from `sklearn_genetic.G

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