R-universe search: topic:explainable-ml

Showing 9 of total 9 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

norskregnesentral

shapr:Prediction Explanation with Dependence-Aware Shapley Values

Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Maintained by Martin Jullum. Last updated 7 days ago.

explainable-ai explainable-ml rcpp rcpparmadillo shapley openblas cpp openmp

154 stars 10.59 score 175 scripts 1 dependents

bgreenwell

fastshap:Fast Approximate Shapley Values

Computes fast (relative to other implementations) approximate Shapley values for any supervised learning model. Shapley values help to explain the predictions from any black box model using ideas from game theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> for details.

Maintained by Brandon Greenwell. Last updated 1 years ago.

explainable-ai explainable-ml interpretable-machine-learning shapley shapley-values variable-importance xai cpp

119 stars 8.65 score 155 scripts 2 dependents

modeloriented

survex:Explainable Machine Learning in Survival Analysis

Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) <doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.

Maintained by Mikołaj Spytek. Last updated 10 months ago.

biostatistics brier-scores censored-data cox-model cox-regression explainable-ai explainable-machine-learning explainable-ml explanatory-model-analysis interpretable-machine-learning interpretable-ml machine-learning probabilistic-machine-learning shap survival-analysis time-to-event variable-importance xai

110 stars 8.40 score 114 scripts

modeloriented

fairmodels:Flexible Tool for Bias Detection, Visualization, and Mitigation

Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.

Maintained by Jakub Wiśniewski. Last updated 2 months ago.

explain-classifiers explainable-ml fairness fairness-comparison fairness-ml model-evaluation

87 stars 7.73 score 51 scripts 1 dependents

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

bgreenwell

ebm:Explainable Boosting Machines

An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) <doi:10.48550/arXiv.1909.09223> for. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.

Maintained by Brandon M. Greenwell. Last updated 29 days ago.

ai blackbox explainable-ai explainable-machine-learning explainable-ml glassbox iml interpretability interpretability-and-explainability interpretable interpretable-ai interpretable-machine-learning interpretable-ml interpretable-models machine-learning xai

1 stars 4.60 score

forestry-labs

distillML:Model Distillation and Interpretability Methods for Machine Learning Models

Provides several methods for model distillation and interpretability for general black box machine learning models and treatment effect estimation methods. For details on the algorithms implemented, see <https://forestry-labs.github.io/distillML/index.html> Brian Cho, Theo F. Saarinen, Jasjeet S. Sekhon, Simon Walter.

Maintained by Theo Saarinen. Last updated 2 years ago.

bart distillation-model explainable-machine-learning explainable-ml interpretability interpretable-machine-learning machine-learning model random-forest xgboost

7 stars 3.92 score 12 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 4 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