yggdrasil-decision-forests VS decision-forests

Compare yggdrasil-decision-forests vs decision-forests and see what are their differences.

yggdrasil-decision-forests

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees. (by google)

decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. (by tensorflow)
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured
yggdrasil-decision-forests decision-forests
4 1
428 651
3.0% 0.9%
9.5 8.3
5 days ago 11 days ago
C++ Python
Apache License 2.0 Apache License 2.0
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.

yggdrasil-decision-forests

Posts with mentions or reviews of yggdrasil-decision-forests. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-05.

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

What are some alternatives?

When comparing yggdrasil-decision-forests and decision-forests you can also consider the following projects:

LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Spearmint - Spearmint Bayesian optimization codebase

tensorflow - An Open Source Machine Learning Framework for Everyone

srbench - A living benchmark framework for symbolic regression

decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest

higgs-logistic-regression

flashlight - A C++ standalone library for machine learning [Moved to: https://github.com/flashlight/flashlight]

interpret - Fit interpretable models. Explain blackbox machine learning.

MLBenchmarks.jl - ML models benchmarks on public dataset