Dev tools · alternatives · 2026
24 alternatives to How to Convert Numerical to Categorical Variables with Decision Trees
How decision trees can be used to convert numerical variables into categorical variables and use R to bin data. Example data set with source code included.
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. How to Convert Numerical to Categorical Variables with Decision Trees launched in 2016; newer entries below may have overtaken it.
- 1SI
Hi All, I would like to share with you a new Python package for interactive decision tree visualization. It is called `supertree`. It visualizes decision tree as interactive graph, where you can collapse and expand selected nodes. You can zoom and pan though large trees. It works with Scikit-learn, Xgboost, and LightGBM. The package works in notebooks: Jupyter Lab, Jupyter Notebook, Google Colab. You can also use it in Python scripts and save output trees to HTML. The package is available on pip: `pip install supertree`. You can find code examples on the GitHub:…
2024 · its alternatives →
- 2
Python Library for Studying Binary Trees. Contribute to joowani/binarytree development by creating an account on GitHub.
2018 · github.com · its alternatives →
- 3
Decision Trees explained. Introduction to the logic and maths behind a Decision Tree classifier.
2020 · programmerbackpack.com · its alternatives →
- 4
Two weeks ago I was on my babymoon in Corfu, Greece. While in transit, I was overseeing a GSoC intern submit an important feature to my array database library, Xarray-SQL. He added `to_dataset()`, which completed the roundtrip between thinking of array data in a tabular model simultaneously as gridded rasters (the premise of the project is that every Nd array can be mapped to 2d, where orthogonal dims of the Nd array are just primary keys of a tabular representation). We discussed in chat, now that this feature existed, what demos could we make that would prove this data model works? With…
Jul 2026 · github.com · its alternatives →
- 5
Program to convert lines of text into a tree structure. - birchb1024/frangipanni
2021 · github.com · its alternatives →
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Lightly▲148Improve your machine learning models by using the right data
2021 · lightly.ai · its alternatives →
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Naive Bayes with support for categorical and continuous data - remykarem/mixed-naive-bayes
2019 · github.com · its alternatives →
- 8
The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.
2020 · github.com · its alternatives →
- 9
Math to Code is an interactive Python tutorial to teach engineers how to read and implement math using the NumPy library.
2020 · mathtocode.com · its alternatives →
- 10
GoLearn▲81Machine Learning for Go. Contribute to sjwhitworth/golearn development by creating an account on GitHub.
2014 · github.com · its alternatives →
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Analyzr▲79Build machine learning models in minutes, with no coding
2023 · analyzr.ai · its alternatives →
- 13
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
2024 · github.com · its alternatives →
- 14
I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com · its alternatives →
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Decision OS — Turning Data into Decisions
Aug 2026 · decisionos-nine.vercel.app · its alternatives →
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Goldbaum et al., (2018). unyt: Handle, manipulate, and convert data with units in Python . Journal of Open Source Software, 3(28), 809, https://doi.org/10.21105/joss.00809
2018 · joss.theoj.org · its alternatives →
- 17SF
I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018 · its alternatives →
- 18
Get a Machine Learning model for your data with single click
2024 · its alternatives →
- 19
How to implement the various methods in real life settings
2016 · its alternatives →
- 20NH
Hi HN! We built Nuanced (https://github.com/nuanced-dev/nuanced), an open-source Python library that makes AI coding tools smarter about code structure. The problem: current AI coding assistants see code as just text. They don't understand which functions call which, or how code components depend on each other. This is why they often suggest changes that break dependencies they should have known about. Nuanced solves this by generating call graphs that map these relationships. When you ask "What would break if I change this function?", instead of guessing, the AI can see…
2025 · nuanced.dev · its alternatives →
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Visions is a python library for working with user defined data type systems. Out of the box, it provides type inference and automated data cleaning of sequence data with backend specific implementations for pandas, spark, python, and numpy. We often use it as a first pass cleaning step when working with tabular data and to simplify the backend logic of both pandas-profiling[1] and our tabular data compression library compressio[2]. Because data types are user defined, we can build user customizable libraries based around types without adding code complexity. In the case of compressio that…
2022 · github.com · its alternatives →
- 22
Credit goes to the contributors of https://github.com/enjeck/num2math for making the tool's core.
2024 · scientific.place · its alternatives →
- 23
QuadTree Model for generating random road network. Contribute to arun1729/road-network development by creating an account on GitHub.
2018 · github.com · its alternatives →
- 24
A tool to automate variable selection for research on large datasets. The provided example is on the variable dictionary of the UK Biobank Dataset. It utilises the GPT-4 API to include/exclude and categorise the 8000+ variables in the UK Biobank. It also provides a concise explanation for each varia
2024 · github.com · its alternatives →
Also compare
- Supertree – interactive visualization of decision trees in Python alternatives
- Python library for learning binarytrees alternatives
- Decision Tree Classifiers Explained alternatives
- I implemented a neural network in SQL alternatives
- I wrote a program to convert lines of text into trees alternatives
- Lightly alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →