• MurTree

  • Referenced in 1 article [sw38488]
  • approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods...
  • See5

  • Referenced in 8 articles [sw12178]
  • speed up the analysis. To maximize interpretability, See5/C5.0 classifiers are expressed as decision trees ... special knowledge of Statistics or Machine Learning (although these don’t hurt, either!) RuleQuest provides...
  • GrASP

  • Referenced in 1 article [sw38091]
  • Interpretable Textual Patterns. Data exploration is an important step of every data science and machine ... learning project, including those involving textual data. We provide a Python library for GrASP...
  • OrdLogReg

  • Referenced in 1 article [sw26696]
  • machine learning methods can model complex interactions, however these models are often difficult to interpret...
  • PiNN

  • Referenced in 2 articles [sw30601]
  • networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces ... molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently...
  • TerpreT

  • Referenced in 2 articles [sw29483]
  • understand the capabilities of machine learning techniques relative to traditional alternatives, such as those based ... representation (declarations of random variables) and an interpreter describing how programs map inputs to outputs ... range of domains, program representations, and interpreter models. Second, it separates the model specification from ... conclude with suggestions for the machine learning community to make progress on program synthesis...
  • PANFIS

  • Referenced in 8 articles [sw13735]
  • PANFIS: A Novel Incremental Learning Machine. Most of the dynamics in real-world systems ... overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning ... presented herein. PANFIS can commence its learning process from scratch with an empty rule base ... transparent rule base escalating human’s interpretability. The learning and modeling performances of the proposed...
  • CoClust

  • Referenced in 3 articles [sw32066]
  • both more accurate and easier to interpret. This paper presents the theory underlying several effective ... easily interface with popular Python machine learning libraries such as scikit-learn...
  • HistoCartography

  • Referenced in 1 article [sw39628]
  • paradigm to describe tissue composition, and learn the tissue structure-to-function relationship. Entity-graphs ... prior pathological knowledge to further support model interpretability and explainability. However, entity-graph analysis requires ... knowledge of state-of-the-art machine learning algorithms applied to graph-structured data, which...
  • TPOT-MDR

  • Referenced in 1 article [sw18807]
  • that TPOT-MDR significantly outperforms modern machine learning methods such as logistic regression and eXtreme ... high-accuracy solution that is also easily interpretable...
  • mikropml

  • Referenced in 1 article [sw38515]
  • Learning Pipelines. An interface to build machine learning models for classification and regression problems. ’mikropml ... tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website ...
  • PyKale

  • Referenced in 1 article [sw39156]
  • interdisciplinary research. We formulate new green machine learning guidelines based on standard software engineering practices ... accurate and interpretable prediction, thus supporting multimodal learning and transfer learning (particularly domain adaptation) with ... latest deep learning and dimensionality reduction models. We build PyKale on PyTorch and leverage ... design enforces standardization and minimalism, embracing green machine learning concepts via reducing repetitions and redundancy...
  • STK

  • Referenced in 2 articles [sw23922]
  • Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using ... other applications areas (such as Geostatistics, Machine Learning, Non-parametric Regression...
  • AI Explainability 360

  • Referenced in 4 articles [sw35201]
  • extensible toolkit for understanding data and machine learning models. As artificial intelligence algorithms make further ... requiring explanations to navigate the space of interpretation and explanation methods, not only those...
  • ROOT

  • Referenced in 4 articles [sw20052]
  • tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package ... step, making use of the interactive C++ interpreter CINT, while running over small data samples...
  • pomegranate

  • Referenced in 2 articles [sw26684]
  • present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling ... learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without ... speed up calculations and releases the global interpreter lock to allow for built-in multithreaded...
  • ActiVis

  • Referenced in 1 article [sw27155]
  • interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating ... ActiVis has been deployed on Facebook’s machine learning platform. We present case studies with...
  • MeLIME

  • Referenced in 1 article [sw38487]
  • domains. Hence, many methodologies for explaining machine learning models have been proposed to address this ... sampling and the use of different local interpretable models. Additionally, we introduce modifications to standard...
  • KATARA

  • Referenced in 1 article [sw37872]
  • using integrity constraints, statistics, or machine learning. These approaches are known to be limited ... table, a KB, and a crowd, interprets table semantics to align it with...
  • CLEVR Parser

  • Referenced in 1 article [sw35072]
  • language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains ... learning and can aid in downstream tasks like language grounding to vision, robotics, compositionality, interpretability...