Requirement of Graphs, corresponding to each of the Implemented Machine-Learning Models

[kwanit1142]

In order to understand more about influence of Parameters, Hyper-Parameters and Input Dataset on a Machine-Learning Model, following Graphs are important : -

1. Loss v/s Number of Iterations.
2. Bias-Variance Tradeoff.
3. Visual Graphs, which highlight the working of a Model on Input Dataset, taken as an Example.

Reference Literatures for Machine Learning Models related Visualizations are mentioned below : -

https://towardsdatascience.com/machine-learning-visualization-fcc39a1e376a

https://towardsdatascience.com/data-visualization-for-machine-learning-and-data-science-a45178970be7

https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_understanding_data_with_visualization.htm

[GoldExplosion]

I would like to work on this. How do I approach this?

[kwanit1142]

@GoldExplosion Read the issue carefully and refer Literature for the type of plots that can be used appropriately. In addition to that, see the Machine-Learning Models in the Repository itself, because each Model will require different Visual representation, alongside their examples too.

[GoldExplosion]

@kwanit1142 ok

[GoldExplosion]

@kwanit1142 i going to keep you posted on what i am doing. right now, i am going to add separate methods for visualisation in each of the models. i will create a separate PR for each model.

[kwanit1142]

That seems to be nice. The Main Thing you have to focus on, is to include Appropriate and purposeful Graphs according to each model. They can be same or different , depending upon their application.

[GoldExplosion]

ok got it

[kwanit1142]

Any Updates @GoldExplosion ?

[GoldExplosion]

i haven't worked on this. i would like not take this issue. sorry.

[kwanit1142]

No worries, Its OK

[kwanit1142]

Completion Phase-1

Linear Regression Plots -> Devyani Gorkar Logistic Regression Plots -> Shreya Sachan Polynomial Regression Plots -> Siddharth Singh