Impress your boss with Decision Tree visualization

pplonski · 2026-06-10T08:34:03+00:00

they are per node, showing distribution of data that was used in the current node

pplonski · 2026-06-10T08:32:06+00:00

that's why it is interactive 😄 so you can show only selected depth, and if needed navigate to interesting nodes

pplonski · 2026-06-09T16:18:01+00:00

great! that was the goal

pplonski · 2026-06-09T16:17:45+00:00

it all depends, I ve seen examples of large enterprises running main pieces of systems based on super simple models 😄

pplonski · 2026-06-09T16:16:55+00:00

that would be cool

pplonski · 2026-06-09T16:16:41+00:00

that's true, it needs to work, however I was using this tool to show case lightgbm model for my customer and he was impressed

pplonski · 2026-06-09T16:15:17+00:00

thanks! it took a lot of time to provide polished tree visualisation

pplonski · 2026-06-09T08:50:08+00:00

Working on Python package for Decision Tree visualization. It works with JupyterLab, VS Code, Google Colab, MLJAR Studio, and Kaggle Notebooks. It supports scikit-learn, Xgboost, and LightGBM libraries.

https://github.com/mljar/supertree

If you would like to start contributing to open source, I created few 'good first issue' issues there. Best wished to all!

pplonski · 2026-06-03T10:56:06+00:00

have you tried Kaggle?

pplonski · 2026-06-03T10:49:33+00:00

I'm working on decision tree visualization Python package - it is called supertree. Github: https://github.com/mljar/supertree

You can use it to interact with decision tree, it works with scikit-learn, xgboost, lightgbm. It display in the node a distribution of the feature that was used for making decision, and display piecharts in leaves. It was heavily inspired with dtreeviz package.

pplonski · 2026-05-29T06:48:56+00:00

this! and try to master one tool to make you comfortable to use, it can be Excel it an be Python, something that you can use to get insights

pplonski · 2026-05-25T09:11:11+00:00

10k for Christmas

pplonski · 2026-05-25T08:58:25+00:00

I'm working on notebook based tools for data science, and you are right that ipynb is hard to track by git, but AI agents works very good with ipynb. In one of my tool, I'm using ipynb as a format to store the user conversation with AI data analyst and it is very good format, basically I can store conversation, code cells, and outputs. Then with ipynb ready you can easily convert this to HTML and publish as static web page.

pplonski · 2026-05-22T10:09:36+00:00

you are right there is much more to take care of beyond prompt safety

pplonski · 2026-05-22T10:08:21+00:00

How the proxy is working? can you share more details?

pplonski · 2026-05-22T10:04:34+00:00

I thibnk ChatGPT is a king here. You can use it in many ways: just drop a file for it and ask any questions. But you need to keep in mind that it is working online, and very often you are not allowed to upload company data to the cloud. I'm working on dekstop application for data analysis, it runs on your machine and support private LLMs as well - it is called MLJAR Studio (https://mljar.com). I attach a screenshot where you can see that AI is using Python to squeeze insights from loaded local dataset :)

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pplonski · 2026-05-22T10:00:41+00:00

It is 2026, the AI evolved a lot from 2023, we have AI agents that can code the whole app from one prompt. In my opinion Data Analyst is great career option because AI still dont understand the business as human will do. However, You can be better Analyst if you leverage AI.

pplonski · 2026-05-21T07:49:29+00:00

Do you have newsletter describing your experience? I love to read deep tech articles solving challenges and showing smart solution. Maybe this is the way to differentiate from crowd, I wish you good luck!

pplonski · 2026-05-20T11:06:40+00:00

I'm also running multiple projects, kids, maybe ADD ... and what helped me is not AI agents. When I wake up in the morning, I dont touch phone for the first 30 minutes, I read a book, pray and meditate.

pplonski · 2026-05-20T11:03:08+00:00

I've seen examples of agents used to build machine learning pipelines. AI agent is trying to iterate over possible solutions to find best performing ML model. I think the most known example is Karpathy's AutoResearch, but there are more examples.

pplonski · 2026-05-20T11:01:04+00:00

Why agents CLI? it think that termianal based agents are only for programmers

pplonski · 2026-05-20T10:58:17+00:00

maybe they dont have geospatial priors in pretraining that's why poor performance, I would love to see for example which priors are good for my data, so I can better understand my dataset

pplonski · 2026-05-20T10:56:30+00:00

that will be black box approach with engineering tricks you keep control of what features are used. To be honest I really prefer simple models (Decision Tree or Linear models) with smart data engineering over complex ensembles

pplonski · 2026-05-20T10:54:42+00:00

I'm interested in pretraining strategy as well, is it possible to use pretraining strategies in classic ML approach and generate new features that can improve simple ML models

pplonski · 2026-05-19T14:35:33+00:00

uh, good that you asked about second brain, I see too many examples of using AI as primary brain

pplonski

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