In #3324 , we had to mark some tests as expected to fail since XGBoost was throwing a FutureWarning. The warning has been addressed in XGBoost, so we're just waiting for the PR merged to be released. This issue is discussed in the #3275 issue.
evalml/tests/component_tests/test_xgboost_classifier.py needs to have the @pytest.mark.xfail removed f
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:
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
Add a description, image, and links to the model-selection topic page so that developers can more easily learn about it.
To associate your repository with the model-selection topic, visit your repo's landing page and select "manage topics."
Description
There are multiple user requests of using GraphNN data (node and edge lists) as sample batches into a custom RLlib model.
https://discuss.ray.io/t/rllib-variable-length-observation-spaces-without-padding/726
https://discuss.ray.io/t/working-with-graph-neural-networks-varying-state-space/5730/2
The recommended method today is to use Repeated observation space and VariableVal