[ENH] Added class weights to feature based classifiers

[lucifer4073]

Reference Issues/PRs

contributes to #1777. Refers PR #1776.

What does this implement/fix? Explain your changes.

The given implementation adds class weights parameter to classification/feature based module.
Code changes for a signature feature based classifier.

class SignatureClassifier(BaseClassifier):
    def __init__(
        self,
        estimator=None,
        augmentation_list=("basepoint", "addtime"),
        window_name="dyadic",
        window_depth=3,
        window_length=None,
        window_step=None,
        rescaling=None,
        sig_tfm="signature",
        depth=4,
        random_state=None,
        class_weight=None,
    ):

Does your contribution introduce a new dependency? If yes, which one?

No

Any other comments?

• Let me know if additional test cases are needed to evaluate class weights parameter.
• FreshPrinceClassifier doesn't have class weights as it uses RotationForestClassifier.

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I have added the following labels to this PR based on the title: [ $\color{#FEF1BE}{\textsf{enhancement}}$ ].
I have added the following labels to this PR based on the changes made: [ $\color{#BCAE15}{\textsf{classification}}$ ]. Feel free to change these if they do not properly represent the PR.

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[lucifer4073]

@all-contributors please add @lucifer4073 for code

[allcontributors]

@lucifer4073

I've put up a pull request to add @lucifer4073! 🎉

[MatthewMiddlehurst]

Can you please verify if all the input values work? does not have to be a test.

[TonyBagnall]

thanks for this, I think a simple test with different call options would be helpful

[hadifawaz1999]

Thanks for this but i think it should be tested to make sure its working properly