Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping

2024-09-14 · Fabi Prezja

Machine learning (ML) research and application often involve time-consuming steps such as model architecture prototyping, feature selection, and dataset preparation. To support these tasks, we introduce the Deep Fast Machine Learning Utils (DFMLU) library, which provides tools designed to automate and enhance aspects of these processes. Compatible with frameworks like TensorFlow, Keras, and Scikit-learn, DFMLU offers functionalities that support model development and data handling. The library includes methods for dense neural network search, advanced feature selection, and utilities for data management and visualization of training outcomes. This manuscript presents an overview of DFMLU's functionalities, providing Python examples for each tool.

📄 PDF Abstract BibTeX arXiv:2409.09537

Code (0)

등록된 구현이 없습니다.

Tasks

feature selectionManagement

Methods 이 논문이 사용한 방법론

Library 설명 없음

abess: A Fast Best Subset Selection Library in Python and R

2021-10-19 · Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang 외

We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis…

Model Selection Sparse Learning Variable Selection

Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library

2020-10-01 · Amir Shahmoradi, Fatemeh Bagheri, Joshua Alexander Osborne

ParaMonte::Python (standing for Parallel Monte Carlo in Python) is a serial and MPI-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for sampling mathematical objective functions, in particular, the pos…

Uncertainty Quantification

RobustX: Robust Counterfactual Explanations Made Easy

2025-02-19 · Junqi Jiang, Luca Marzari, Aaryan Purohit, Francesco Leofante

The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) are ideally suited for this, as they can o…

counterfactual Decision Making

Little Ball of Fur: A Python Library for Graph Sampling

2020-06-08 · CIKM 2020 10 · Benedek Rozemberczki, Oliver Kiss, Rik Sarkar

Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploratio…

Graph Classification Graph Embedding Graph Sampling Node Classification

LIBTwinSVM: A Library for Twin Support Vector Machines

2020-01-27 · Amir M. Mir, Mahdi Rahbar, Jalal A. Nasiri

This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs). Our library provides a set of useful functionalities such as fast TSVMs estimators, model selection, vi…

General Classification Model Selection