• InterpretML

  • Referenced in 7 articles [sw30904]
  • InterpretML: A Unified Framework for Machine Learning Interpretability- InterpretML is an open-source Python package ... which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types ... glassbox models, which are machine learning models designed for interpretability (ex: linear models, rule lists ... package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified...
  • iml

  • Referenced in 6 articles [sw28966]
  • package iml: Interpretable Machine Learning. Interpretability methods to analyze the behavior and predictions...
  • shapper

  • Referenced in 2 articles [sw30900]
  • machine learning models. In applied machine learning, there is a strong belief that we need ... strike a balance between interpretability ... accuracy. However, in field of the Interpretable Machine Learning, there are more and more...
  • Skater

  • Referenced in 2 articles [sw33036]
  • model to help one build an Interpretable machine learning system often needed for real world ... actively working towards to enabling faithful interpretability for all forms models). It is an open...
  • ProtoAttend

  • Referenced in 1 article [sw37082]
  • learning. We propose a novel inherently interpretable machine learning method that bases decisions ... original model: (1)it enables high-quality interpretability that outputs samples most relevant...
  • ENDER

  • Referenced in 15 articles [sw12831]
  • machine learning. The main advantage of decision rules is their simplicity and human-interpretable form...
  • DALEXtra

  • Referenced in 1 article [sw35212]
  • various machine learning models. In applied machine learning, there is a strong belief that ... need to strike a balance between interpretability ... accuracy. However, in field of the interpretable machine learning, there are more and more...
  • ORL

  • Referenced in 4 articles [sw28404]
  • Learning customized and optimized lists of rules with mathematical programming. We introduce a mathematical programming ... type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • been introduced within the context of statistical learning theory and structural risk minimization ... typically quadratic programs. Least Squares Support Vector Machines (LS-SVM) are reformulations to the standard ... additionally emphasize and exploit primal-dual interpretations. Links between kernel versions of classical pattern recognition ... Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness...
  • live

  • Referenced in 2 articles [sw27469]
  • Interpretable (Model-Agnostic) Visual Explanations. Interpretability of complex machine learning models is a growing concern...
  • Alibi

  • Referenced in 2 articles [sw35451]
  • Python library aimed at machine learning model inspection and interpretation. The focus of the library...
  • OpenML-CC18

  • Referenced in 1 article [sw37870]
  • OpenML Benchmarking Suites. Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks ... curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and reporting...
  • DIVCLUS-T

  • Referenced in 5 articles [sw02736]
  • interpretation is studied by applying the three algorithms on six databases from the UCI Machine ... Learning repository...
  • DALEX

  • Referenced in 12 articles [sw26094]
  • package DALEX: Descriptive mAchine Learning EXplanations. Machine Learning (ML) models are widely used and have ... such black-box models usually lack of interpretability. DALEX package contains various explainers that help...
  • mvdalab

  • Referenced in 0 articles [sw15898]
  • tools , and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide...
  • EXPATS

  • Referenced in 1 article [sw38107]
  • their interpretability of models and predictions, traditional machine learning (ML) algorithms based on handcrafted features ... objectives (regression and classification), although modern deep learning frameworks such as PyTorch require deep ... also provides seamless integration with the Language Interpretability Tool (LIT) so that one can interpret...
  • AutoGraph

  • Referenced in 3 articles [sw30957]
  • scalable or fast to execute. In machine learning, imperative style libraries like Autograd and PyTorch ... easy to write, but suffer from high interpretive overhead and are not easily deployable...
  • GNNExplainer

  • Referenced in 1 article [sw37864]
  • GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with ... interpretable explanations for predictions of any GNN-based model on any graph-based machine learning ... ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty...
  • 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...