Necessities of Score Metrics, for evaluation of ML Classifier Models

[kwanit1142]

Reference Literatures for this issue are as follows : -

https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234

https://towardsdatascience.com/20-popular-machine-learning-metrics-part-1-classification-regression-evaluation-metrics-1ca3e282a2ce <---------------------(Refer this first and foremost)

[Prachi0203]

I want to work on this for now I want to add accuracy,precision,recall, log loss, mean squared log error, mean percentage error metrics

[kwanit1142]

@Prachi0203 , apart from the work you done, there are still more to cover, that's why the Literature for respective issue is referred, so do check them and add other Score Metric components too, like F2 Score, Z-Score, etc.

[RATED-R-SUNDRAM]

hey i just wanted to ask that all these necessities have to be included in models.py ? could u guide me to exact task associated with the issue.

[kwanit1142]

@RATED-R-SUNDRAM , see #76 , although pull requested, you can still get insight.

In short, you have to make a separate file, which can be used as an import by most of the models, to showcase their scores.

[ssiddharth27]

I would like to work on this issue.

[kwanit1142]

Okay, @Siddharth-Singh27

[kwanit1142]

Closing the issue, as it's PR is merged.