autoai
Model finder
A Python-based framework for automating the process of finding and training the best-performing machine learning model for regression and classification tasks on numerical data.
Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
176 stars
6 watching
42 forks
Language: Python
last commit: 4 months ago
Linked from 1 awesome list
aiautoaiautomlcodegendeep-learningmachine-learningmlpython
Related projects:
Repository | Description | Stars |
---|---|---|
| Automates model building and deployment process by optimizing hyperparameters and compressing models for edge computing. | 200 |
| Automates machine learning model creation and optimization for complex datasets | 1,857 |
| An ALE plot generation tool for explaining machine learning model predictions | 160 |
| An autoML system designed to build the best model from a given classification problem and dataset | 528 |
| An interactive tool to analyze and compare the performance of natural language processing models | 362 |
| Automates machine learning model training using pre-set configurations and modular design. | 159 |
| A Python package implementing an interpretable machine learning model for text classification with visualization tools | 336 |
| Automates the search for optimal neural network configurations in deep learning applications | 468 |
| A curated list of large machine learning models tracked over time | 341 |
| An autoML framework for building and analyzing trading systems and sports betting models using machine learning algorithms. | 1,170 |
| Automates cost modeling and optimization for indexers in blockchain networks using reinforcement learning and GraphQL APIs. | 11 |
| A versatile library designed to streamline the integration and interaction of diverse AI models for collaborative software development | 21 |
| An open-source framework for adapting representation models to various tasks and industries | 1,743 |
| This project implements a deep metric learning framework using an adversarial auxiliary loss to improve robustness. | 39 |
| Automatically builds multiple machine learning models using a single line of code. | 526 |