R-universe search: topic:explainable-artificial-intelligence

Showing 5 of total 5 results (show query)

modeloriented

DALEX:moDel Agnostic Language for Exploration and eXplanation

Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <https://jmlr.org/papers/v19/18-416.html>.

Maintained by Przemyslaw Biecek. Last updated 2 months ago.

black-box dalex data-science explainable-ai explainable-artificial-intelligence explainable-ml explanations explanatory-model-analysis fairness iml interpretability interpretable-machine-learning machine-learning model-visualization predictive-modeling responsible-ai responsible-ml xai

1.4k stars 13.40 score 876 scripts 21 dependents

modeloriented

auditor:Model Audit - Verification, Validation, and Error Analysis

Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.

Maintained by Alicja Gosiewska. Last updated 1 years ago.

classification error-analysis explainable-artificial-intelligence machine-learning model-validation regression-models residuals xai

58 stars 8.76 score 94 scripts 2 dependents

modeloriented

treeshap:Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm

An efficient implementation of the 'TreeSHAP' algorithm introduced by Lundberg et al., (2020) <doi:10.1038/s42256-019-0138-9>. It is capable of calculating SHAP (SHapley Additive exPlanations) values for tree-based models in polynomial time. Currently supported models include 'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'.

Maintained by Mateusz Krzyzinski. Last updated 1 years ago.

explainability explainable-ai explainable-artificial-intelligence explanatory-model-analysis iml interpretability interpretable-machine-learning machine-learning responsible-ml shap shapley-value xai cpp

83 stars 6.69 score 170 scripts

modeloriented

vivo:Variable Importance via Oscillations

Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile and global variable importance measure based on Partial Dependence Profiles.

Maintained by Anna Kozak. Last updated 5 years ago.

explainable-ai explainable-artificial-intelligence explainable-ml iml interpretable-machine-learning variable-importance xai

14 stars 5.45 score 7 scripts

haghish

HMDA:Holistic Multimodel Domain Analysis for Exploratory Machine Learning

Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) <doi:10.13140/RG.2.2.32473.63846>.

Maintained by E. F. Haghish. Last updated 3 days ago.

ensemble-feature-importance explainable-ai explainable-artificial-intelligence explainable-machine-learning explainable-ml exploratory-machine-learning exploratory-modelling feature-importance feature-selection-methods holistic-modeling holistic-multimodel-domain-analysis multimodel-ensemble reproducible-ai reproducible-research robust-feature-selection shapley-additive-explanations shapley-values transparent-ai weighted-mean-shap wmshap

1 stars 3.54 score