random-forest

Open-source projects categorized as random-forest
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Language: + Python + Jupyter Notebook + TypeScript + C++ + C# + Julia + Ruby + R
Topics: Machine Learning Python decision-trees Data Science scikit-learn
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Top 23 random-forest Open-Source Projects

  • H2O

    H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

    • Project mention: Really struggling with open source models | /r/LocalLLaMA | 2023-07-12

    I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/

  • orange

    🍊 :bar_chart: :bulb: Orange: Interactive data analysis

    • Project mention: Hierarchical Clustering | news.ycombinator.com | 2024-04-20

    I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

    Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

    https://orangedatamining.com/

    https://orange3.readthedocs.io/projects/orange-visual-progra...

  • 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.

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  • FLAML

    A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

    • Project mention: AutoGen: Enabling Next-Gen GPT-X Applications | news.ycombinator.com | 2023-08-22

    I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.

    Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.

    Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.

    This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...

  • mljar-supervised

    Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

    • Project mention: Show HN: Web App with GUI for AutoML on Tabular Data | news.ycombinator.com | 2023-08-24

    Web App is using two open-source packages that I've created:

    - MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised

    - Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury

    You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.

  • dtreeviz

    A python library for decision tree visualization and model interpretation.

    • Project mention: Dtreeviz: Decision Tree Visualization | news.ycombinator.com | 2023-08-03

  • awesome-fraud-detection-papers

    A curated list of data mining papers about fraud detection.

    • Project mention: awesome-fraud-detection-papers: NEW Extended Research - star count:1346.0 | /r/algoprojects | 2023-05-13

  • SMAC3

    SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • awesome-gradient-boosting-papers

    A curated list of gradient boosting research papers with implementations.

  • decision-forests

    A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

  • machinelearnjs

    Machine Learning library for the web and Node.

  • FastTreeSHAP

    Fast SHAP value computation for interpreting tree-based models

  • yggdrasil-decision-forests

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

    • Project mention: Why do tree-based models still outperform deep learning on tabular data? (2022) | news.ycombinator.com | 2024-03-05

    Is it this library https://github.com/google/yggdrasil-decision-forests ?

  • emlearn

    Machine Learning inference engine for Microcontrollers and Embedded devices

  • SharpLearning

    Machine learning for C# .Net

  • linear-tree

    A python library to build Model Trees with Linear Models at the leaves.

    • Project mention: Is there any algorithm that combines decision trees with regression models? | /r/learnmachinelearning | 2023-06-06

    Sure is! Here’s an implementation

  • Intrusion-Detection-System-Using-Machine-Learning

    Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

  • miceforest

    Multiple Imputation with LightGBM in Python

  • shapley

    The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

  • STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

    Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

    • Project mention: STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA: NEW Other Models - star count:100.0 | /r/algoprojects | 2023-04-29

  • DroidDetective

    A machine learning malware analysis framework for Android apps.

  • BetaML.jl

    Beta Machine Learning Toolkit

  • Scoruby

    Ruby Scoring API for PMML

  • aorsf

    Accelerated Oblique Random Survival Forests

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

random-forest related posts

Index

What are some of the best open-source random-forest projects? This list will help you:

Project Stars
1 H2O 6,730
2 orange 4,604
3 FLAML 3,671
4 mljar-supervised 2,929
5 dtreeviz 2,836
6 awesome-fraud-detection-papers 1,545
7 SMAC3 1,003
8 awesome-gradient-boosting-papers 980
9 decision-forests 650
10 machinelearnjs 536
11 FastTreeSHAP 492
12 yggdrasil-decision-forests 423
13 emlearn 374
14 SharpLearning 373
15 linear-tree 323
16 Intrusion-Detection-System-Using-Machine-Learning 320
17 miceforest 308
18 shapley 210
19 STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA 116
20 DroidDetective 98
21 BetaML.jl 90
22 Scoruby 68
23 aorsf 31

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