There are 15 repositories under model-selection topic.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
A collection of computer vision pre-trained models.
Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.
LAMA - automatic model creation framework
A comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
Human-explainable AI.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
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Dynamic Nested Sampling package for computing Bayesian posteriors and evidences
ML hyperparameters tuning and features selection, using evolutionary algorithms.
State-of-the art Automated Machine Learning python library for Tabular Data
A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.
pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Fit and compare complex models reliably and rapidly. Advanced nested sampling.
Measure and visualize machine learning model performance without the usual boilerplate.
Mixing Language Models with Self-Verification and Meta-Verification
Python library for Bayesian hyper-parameters optimization
Random Forests in Apache Spark
DataFrame support for scikit-learn.
A Bayesian model+algorithm for community detection in bipartite networks
Bayesian X-ray analysis (nested sampling for Xspec and Sherpa)
Code for "Unsupervised Model Selection for Time-series Anomaly Detection", ICLR 2023.
This repository will contain all the stuffs required for beginners in ML and DL do follow and star this repo for regular updates
[ICML 2020] InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs