![](https://github.com/modeloriented/dalex/raw/HEAD/man/figures/logo.png)
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>.
Last updated 3 months ago
black-boxdalexdata-scienceexplainable-aiexplainable-artificial-intelligenceexplainable-mlexplanationsexplanatory-model-analysisfairnessimlinterpretabilityinterpretable-machine-learningmachine-learningmodel-visualizationpredictive-modelingresponsible-airesponsible-mlxai
1.4k stars 10.06 score 35 dependencies 20 dependents![](https://github.com/modeloriented/ibreakdown/raw/HEAD/man/figures/logo.png)
iBreakDown - Model Agnostic Instance Level Variable Attributions
Model agnostic tool for decomposition of predictions from black boxes. Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>, with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420> This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Last updated 9 months ago
breakdownimlinterpretabilityshapleyxai
79 stars 4.90 score 28 dependencies 21 dependentsbreakDown - Model Agnostic Explainers for Individual Predictions
Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models.
Last updated 6 months ago
data-scienceimlinterpretabilitymachine-learningvisual-explanationsxai
102 stars 4.36 score 28 dependencies 2 dependentsceterisParibus - Ceteris Paribus Profiles
Ceteris Paribus Profiles (What-If Plots) are designed to present model responses around selected points in a feature space. For example around a single prediction for an interesting observation. Plots are designed to work in a model-agnostic fashion, they are working for any predictive Machine Learning model and allow for model comparisons. Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
Last updated 4 years ago
41 stars 2.92 score 38 dependenciesdrifter - Concept Drift and Concept Shift Detection for Predictive Models
Concept drift refers to the change in the data distribution or in the relationships between variables over time. 'drifter' calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. 'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Last updated 5 years ago
concept-driftconcept-shiftdrwhypredictive-modeling
19 stars 2.15 score 44 dependencies 1 dependentslocalModel - LIME-Based Explanations with Interpretable Inputs Based on Ceteris Paribus Profiles
Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable inputs are created based on local rather than global behaviour of each original feature.
Last updated 3 years ago
14 stars 1.89 score 47 dependenciesSmarterPoland - Tools for Accessing Various Datasets Developed by the Foundation SmarterPoland.pl
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Last updated 1 years ago
9 stars 1.88 score 40 dependencies 2 dependentsddst - Data Driven Smooth Tests
Smooth tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find two groups of smooth of test: goodness-of-fit tests and nonparametric tests for comparing distributions. Among goodness-of-fit tests there are tests for exponent, Gaussian, Gumbel and uniform distribution. Among nonparametric tests there are tests for stochastic dominance, k-sample test, test with umbrella alternatives and test for change-point problems.
Last updated 1 years ago
data-drivensmooth-teststatisticstest
6 stars 1.66 score 31 dependencies 2 dependentsPBImisc - A Set of Datasets Used in My Classes or in the Book 'Modele Liniowe i Mieszane w R, Wraz z Przykladami w Analizie Danych'
A set of datasets and functions used in the book 'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'. Datasets either come from real studies or are created to be as similar as possible to real studies.
Last updated 8 years ago
1.02 score 10 dependencies 1 dependentsPrzewodnik - Datasets and Functions Used in the Book 'Przewodnik po Pakiecie R'
Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at <http://biecek.pl/R>. Among others you will find here data about housing prices, cancer patients, running times and many others.
Last updated 8 years ago
0.00 score 52 dependencies