model-selection

According to FastAPI's docs, response_model can accept type annotations that are not pydantic models. However, the code referenced below is checking for the __fields__ attribute, which won't be on type annotations such as list[float], for example.

https://github.com/ray-project/ray/blob/e60a5f52eb93c851b186cb78fa1f70d

Follow up on alteryx/evalml#3182 based on @freddyaboulton's comment:

I think we can improve this implementation. Right now we do two scans of the data to determine the highly null columns and the columns with any nulls. Doing so also involves creating a new copy of the data without NaturalLanguage/DateTime.

I think if we refactor get_null_column_information to retu

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