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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)
Drawing Survival Curves using 'ggplot2'
Contains the function 'ggsurvplot()' for drawing easily beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. Other functions are also available to plot adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
Effects and Importances of Model Ingredients
Collection of tools for assessment of feature importance and feature effects.
Key functions are:
feature_importance() for assessment of global level feature importance,
ceteris_paribus() for calculation of the what-if plots,
partial_dependence() for partial dependence plots,
conditional_dependence() for conditional dependence plots,
accumulated_dependence() for accumulated local effects plots,
aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
generic print() and plot() for better usability of selected explainers,
generic plotD3() for interactive, D3 based explanations, and
generic describe() for explanations in natural language.
The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018)
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)
Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
Two partially supervised mixture modeling methods:
soft-label and belief-based modeling are implemented.
For completeness, we equipped the package also with the
functionality of unsupervised, semi- and fully supervised
mixture modeling. The package can be applied also to selection
of the best-fitting from a set of models with different
component numbers or constraints on their structures.
For detailed introduction see:
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software
Tools for Storing, Restoring and Searching for R Objects
Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. The more projects we work with the more artifacts are produced and the harder it is to manage these artifacts. Archivist helps to store and manage artifacts created in R. Archivist allows you to store selected artifacts as a binary files together with their metadata and relations. Archivist allows to share artifacts with others, either through shared folder or github. Archivist allows to look for already created artifacts by using it's class, name, date of the creation or other properties. Makes it easy to restore such artifacts. Archivist allows to check if new artifact is the exact copy that was produced some time ago. That might be useful either for testing or caching.
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.
Data Driven Smooth Tests
Smooth testing of goodness of fit. These tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find various tests for exponent, Gaussian, Gumbel and uniform distribution.
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)
DataCrunchers (PogromcyDanych) is the Massive Online Open Course that Brings R and Statistics to the People
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.