Topic: "automl-pipeline"
upgini/upgini
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
Language: Python - Size: 164 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 331 - Forks: 25
UrbsLab/STREAMLINE
Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data
Language: Jupyter Notebook - Size: 595 MB - Last synced at: 18 days ago - Pushed at: 18 days ago - Stars: 76 - Forks: 11
PKU-DAIR/mindware Fork of thomas-young-2013/mindware
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Language: Python - Size: 62.9 MB - Last synced at: 9 days ago - Pushed at: over 3 years ago - Stars: 56 - Forks: 9
TsLu1s/atlantic
Atlantic: Automated Data Preprocessing Framework for Machine Learning
Language: Python - Size: 1.94 MB - Last synced at: 7 days ago - Pushed at: 4 months ago - Stars: 29 - Forks: 4
TsLu1s/tsforecasting
TSForecasting: Automated Time Series Forecasting Framework
Language: Python - Size: 256 KB - Last synced at: 6 days ago - Pushed at: 6 months ago - Stars: 28 - Forks: 1
david-thrower/cerebros-core-algorithm-alpha
The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.
Language: Jupyter Notebook - Size: 56.6 MB - Last synced at: 23 days ago - Pushed at: 23 days ago - Stars: 27 - Forks: 3
IBM/sail
Library for streaming data and incremental learning algorithms.
Language: Python - Size: 28.7 MB - Last synced at: 15 days ago - Pushed at: about 1 year ago - Stars: 23 - Forks: 12
Niklauseik/FiLM-Benchmark
Benchmark pipeline for evaluating language models on financial tasks, including sentiment analysis and credit scoring. Supports over ten tasks with modular design for easy integration of new tasks. Provides automated performance metrics for standardized evaluation, benefiting researchers and practitioners in finance.
Language: Python - Size: 44.9 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 10 - Forks: 1
thompson0012/PyEmits
Sugar candy for data scientist. Easy manipulation in time-series data analytics works.
Language: Python - Size: 4.07 MB - Last synced at: 8 days ago - Pushed at: about 1 year ago - Stars: 8 - Forks: 1
jianzhnie/AutoTimm
Auto torch image models: train and evaluation
Language: Python - Size: 399 KB - Last synced at: about 2 months ago - Pushed at: over 3 years ago - Stars: 8 - Forks: 3
CleverInsight/predicteasy
Powerful AutoML toolkit
Language: Jupyter Notebook - Size: 308 KB - Last synced at: 13 days ago - Pushed at: 10 months ago - Stars: 4 - Forks: 2
g0bel1n/TinyAutoML
TinyAutoML is a comprehensive Pipeline Classifier Project thought as a Scikit-learn plugin
Language: Python - Size: 2.73 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 4 - Forks: 0
rdattafl/SNP-Data-Analysis-Project
A GitHub compiling the input data, Python and Jupyter Notebook scripts, and all relevant statistical outputs from running the AutoMLPipe-BC automated machine learning pipeline (from the Urbanowicz Lab - https://github.com/UrbsLab) on a large-scale single nucleotide polymorphism (SNP) dataset from patients with congenital heart disease (CHD)
Language: Jupyter Notebook - Size: 3.84 MB - Last synced at: 4 months ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 0
eurobios-mews-labs/palma
This library aims at providing tools for an automatic machine learning approach. As many tools already exist to establish one or the other component of an AutoML approach, the idea of this library is to provide a structure rather than to implement a complete service.
Language: Python - Size: 18.2 MB - Last synced at: 3 days ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 5
dzaridis/simplatab-machine-learning-automator
Simplatab: An Automated & Explainable Machine Learning Framework
Language: Python - Size: 246 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 0
zgornel/Kdautoml.jl
Knowledge-driven AutoML
Language: Julia - Size: 166 KB - Last synced at: about 2 months ago - Pushed at: 8 months ago - Stars: 2 - Forks: 0
EugenHotaj/daas
AutoML as a Service.
Language: Python - Size: 85.9 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 1
dinaabdulrasoul/Bank-Marketing-Campaigns-ML-Models
This project aims to create Machine Learning models using Azure's AutoML to find the best model that fits the data and Hypderdrive to find the best hyperparameters.
Language: Jupyter Notebook - Size: 3.14 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0
selmantabet/mjolnir-automl
Project Mjölnir: An Automated Brute-Force Dataset-Model Combinatorics Training and Evaluation Pipeline for Computer Vision
Language: Jupyter Notebook - Size: 29.6 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 1 - Forks: 0
Eva-Kaushik/AutoML
AutoML
Language: Python - Size: 13.1 MB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0
blurred-machine/shrinkit
Shrinkit is a powerful GUI-based Python library designed for automating machine learning tasks. With its intuitive interface, Shrinkit simplifies the process of building, training, and evaluating machine learning models, making it accessible to users of all skill levels. Shrinkit is a No-code package which can be used as a GUI.
Language: Python - Size: 226 KB - Last synced at: 11 days ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0
fernandonieuwveldt/mlxops-pipeline
Automating the ML Training Lifecycle with MLxOPS
Language: Python - Size: 1.56 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0
vxyagr/pintar.ai-automl
Auto Machine learning platform as seen on https://www.youtube.com/watch?v=JHJLLiMnz6A
Language: JavaScript - Size: 4.97 MB - Last synced at: about 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0
SamoraHunter/ml_binary_classification_gridsearch_hyperOpt
Automated machine learning. Evaluate a battery of binary classification algorithms across feature and hyper-parameter spaces.
Language: Python - Size: 1.01 MB - Last synced at: 20 days ago - Pushed at: 20 days ago - Stars: 0 - Forks: 0
SaintAngeLs/CS-MINI-2024Z-AutoML_project_1
Analyze the tunability of machine learning models with Grid Search, Random Search, and Bayesian Optimization. This project explores hyperparameter tuning methods on diverse datasets, comparing efficiency, stability, and performance. Featuring Random Forest, XGBoost, Elastic Net, and Gradient Boosting.
Language: Python - Size: 9.16 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0
Ankurac7/AutoML
Automated ML pipeline
Language: Jupyter Notebook - Size: 235 KB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0
ongaunjie1/pycaret_automl_streamlit
Utilizes pycaret to automates machine learning workflows (Deployed at streamlit)
Language: Jupyter Notebook - Size: 341 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
chollette/Azure-Machine-Learning-Bank-Marketing-Classification
This is a Bank Marketing Machine Learning Classification Project in fulfillment of the Udacity Azure ML Nanodegree. In this project, you will learn to utilize Azure Machine Learning Studio and Azure Python SDK to create classifier models from scratch. The files and documentation with experiment instructions needed for replicating the project is provided for you.
Language: Jupyter Notebook - Size: 2.31 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 1
