problexity -- an open-source Python library for binary classification
problem complexity assessment
- URL: http://arxiv.org/abs/2207.06709v1
- Date: Thu, 14 Jul 2022 07:32:15 GMT
- Title: problexity -- an open-source Python library for binary classification
problem complexity assessment
- Authors: Joanna Komorniczak, Pawel Ksieniewicz
- Abstract summary: The classification problem's complexity assessment is an essential element of many topics in the supervised learning domain.
The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages.
This paper describes the software module that allows for the estimation of 22 complexity measures for the Python language.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The classification problem's complexity assessment is an essential element of
many topics in the supervised learning domain. It plays a significant role in
meta-learning -- becoming the basis for determining meta-attributes or
multi-criteria optimization -- allowing the evaluation of the training set
resampling without needing to rebuild the recognition model. The tools
currently available for the academic community, which would enable the
calculation of problem complexity measures, are available only as libraries of
the C++ and R languages. This paper describes the software module that allows
for the estimation of 22 complexity measures for the Python language --
compatible with the scikit-learn programming interface -- allowing for the
implementation of research using them in the most popular programming
environment of the machine learning community.
Related papers
- regAL: Python Package for Active Learning of Regression Problems [0.0]
Python package regAL allows users to evaluate different active learning strategies for regression problems.
We present our Python package regAL, which allows users to evaluate different active learning strategies for regression problems.
arXiv Detail & Related papers (2024-10-23T14:34:36Z) - Towards Identifying Code Proficiency through the Analysis of Python Textbooks [7.381102801726683]
The aim is to gauge the level of proficiency a developer must have to understand a piece of source code.
Prior attempts, which relied heavily on expert opinions and developer surveys, have led to considerable discrepancies.
This paper presents a new approach to identifying Python competency levels through the systematic analysis of introductory Python programming textbooks.
arXiv Detail & Related papers (2024-08-05T06:37:10Z) - A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning [42.350737545269105]
We show how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.
arXiv Detail & Related papers (2024-07-19T23:01:48Z) - SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks.
It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches.
We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
arXiv Detail & Related papers (2023-04-21T10:00:22Z) - Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher
Education Programming Courses? [6.2122699483618]
We evaluated the capability of generative pre-trained transformers (GPT) to pass assessments in Python programming courses at the postsecondary level.
We studied if and how successfully GPT models leverage feedback provided by an auto-grader.
It is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score.
arXiv Detail & Related papers (2023-03-16T13:58:45Z) - Small-Text: Active Learning for Text Classification in Python [23.87081733039124]
small-text is an easy-to-use active learning library for Python.
It offers pool-based active learning for single- and multi-label text classification.
arXiv Detail & Related papers (2021-07-21T19:23:56Z) - Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis.
When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
arXiv Detail & Related papers (2021-06-18T15:08:47Z) - Variable-Shot Adaptation for Online Meta-Learning [123.47725004094472]
We study the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
We find that meta-learning solves the full task set with fewer overall labels and greater cumulative performance, compared to standard supervised methods.
These results suggest that meta-learning is an important ingredient for building learning systems that continuously learn and improve over a sequence of problems.
arXiv Detail & Related papers (2020-12-14T18:05:24Z) - Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
We introduce a fast optimization-based meta-learning method for few-shot classification.
Our strategy enables important aspects of the base learner objective to be learned during meta-training.
We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
arXiv Detail & Related papers (2020-10-01T15:59:31Z) - BOML: A Modularized Bilevel Optimization Library in Python for Meta
Learning [52.90643948602659]
BOML is a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework.
It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods.
arXiv Detail & Related papers (2020-09-28T14:21:55Z) - OPFython: A Python-Inspired Optimum-Path Forest Classifier [68.8204255655161]
This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython.
As OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language.
arXiv Detail & Related papers (2020-01-28T15:46:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.