decision-forests VS best-of-ml-python

Compare decision-forests vs best-of-ml-python and see what are their differences.

decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. (by tensorflow)
Python Machine Learning random-forest Tensorflow ML decision-trees gradient-boosting Interpretability decision-forest Keras
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best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. (by ml-tooling)
Python Machine Learning Data Science NLP Data Visualization Tensorflow Pytorch scikit-learn Keras data-visualizations Deep Learning Automl ML Jax Transformer Data Analysis chatgpt Gpt gpt-3
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ml-python.best-of.org
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decision-forests best-of-ml-python
1 16
651 15,335
0.9% 0.7%
8.3 7.8
9 days ago 8 days ago
Python Python
Apache License 2.0 Creative Commons Attribution Share Alike 4.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

decision-forests

Posts with mentions or reviews of decision-forests. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-03.
  • Why do tree-based models still outperform deep learning on tabular data?
    5 projects | news.ycombinator.com | 3 Aug 2022
    I can't explain it, but I help maintain TensorFlow Decision Forests [1] and Yggdrasil Decision Forests [2], and in an AutoML system at work that trains models on lots of various users data, decision forest models gets selected as best (after AutoML tries various model types and hyperparameters) somewhere between 20% to 40% of the times, systematically. It's pretty interesting. Other ML types considered are NN, linear models (with auto feature crossings generation), and a couple of other variations.

    [1] https://github.com/tensorflow/decision-forests

best-of-ml-python

Posts with mentions or reviews of best-of-ml-python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-10.

What are some alternatives?

When comparing decision-forests and best-of-ml-python you can also consider the following projects:

Spearmint - Spearmint Bayesian optimization codebase

Awesome-WAF - 🔥 Web-application firewalls (WAFs) from security standpoint.

yggdrasil-decision-forests - A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

srbench - A living benchmark framework for symbolic regression

dtale - Visualizer for pandas data structures

higgs-logistic-regression

ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)

awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.

kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

NBA-Machine-Learning-Sports-Betting - NBA sports betting using machine learning

livelossplot - Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

decision-forests vs Spearmint best-of-ml-python vs Awesome-WAF decision-forests vs yggdrasil-decision-forests best-of-ml-python vs ktrain decision-forests vs srbench best-of-ml-python vs dtale decision-forests vs higgs-logistic-regression best-of-ml-python vs ffcv best-of-ml-python vs awesome-python best-of-ml-python vs kmodes best-of-ml-python vs NBA-Machine-Learning-Sports-Betting best-of-ml-python vs livelossplot