nidhaloff

Interesting opinion. Well I must disagree in some points. First, yes sure the tool uses declarative paradigm, which is the goal of the project. If you want to use ML without writing code, then you will certainly not want an imperative framework.

Second, I must disagree that most other common frameworks are imperative. I would say it's a mix of declarative & imperative but certainly not imperative.

Finally, it's interesting how you see this as a just abstraction tool. I find other ML frameworks are more about abstraction since you are focusing on building your model but all details are hidden from you using the framework. Sure, igel is also about abstraction but to say it's JUST abstraction? mmm I find it not quite right, instead it's more about automating the stuff that you would write yourself using other frameworks.

At the end of the day, we all have different opinions and feedback is important ;)

Hi, and what was the result/conclusion of the discussion? I'm interested in your finding, is it considered code or not :D

Hi, thanks for your comment. I actually understood that he meant something like a hyperparameter search/tuning using cross validation (at least that what came in my mind).

Well. The user is writing a description in a human readable format. Then, the tool will take that description and start running the pipeline. From data reading, preprocessing until creating and evaluating the model. If this isn't automation, please define automation for me.

Also there are new features that I'm working on. The stable release was done this week.

this is a great feature! Thanks for the feedback

It's not about that someone will have difficulty using sklearn. It's more about how clean the approach is if you have all your configs in a yaml file and you can change things very easily/quickly and rerun an experiment. I'm working with data & ML models everyday and it became overwhelming when my codebase is large and I want to change small things and re-run an experiment. Also It would be great to not lose much time writing that code in the first place (although it's easy to do), if you want a quick and dirty draft. The thing is, it is much cleaner if you have your preprocessing methods and model definition in one file. However, there are other features that will be integrated soon, like a simple gui built in python

I agree. That's why some usually used pre-processing methods were implemented in the stable release.. and more is yet to come

already working on a gui that users can launch using a command. You can check the issues list

Create machine learning models on the fly. Fast prototypes creation and test proof of concepts without spending much time preprocessing data and modifying models or parameters manually.

I created this package because I needed to automate a translation task. There are other libraries out there for this. However, I wanted a tool, which gives the flexibility to switch between different translators. Therefore, I created the deep-translator package, in which many translators are integrated. I appreciate any feedback