https://github.com/pbiecek/breakdown

Model Agnostics breakDown plots
https://github.com/pbiecek/breakdown

data-science iml interpretability machine-learning visual-explanations xai

Last synced: 13 days ago
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Model Agnostics breakDown plots

• Host: GitHub
• URL: https://github.com/pbiecek/breakdown
• Owner: pbiecek
• Created: 2017-11-18T09:37:00.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-03-12T21:20:53.000Z (8 months ago)
• Last Synced: 2024-10-16T03:46:00.519Z (28 days ago)
• Topics: data-science, iml, interpretability, machine-learning, visual-explanations, xai
• Language: R
• Homepage: https://pbiecek.github.io/breakDown/
• Size: 20.2 MB
• Stars: 103
• Watchers: 11
• Forks: 16
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

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README

        
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# Break Down: Model Agnostic Explainers for Individual Predictions

The `breakDown` package is a 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 works for binary classifiers and general regression models. 

Find lots of R examples at `breakDown` website: https://pbiecek.github.io/breakDown/

Interested in the methodology? Find the math behind `breakDown` and `live` at: https://arxiv.org/abs/1804.01955

Looking for the `python` version of Break Down? Find it here: https://github.com/bondyra/pyBreakDown

**New generation of the Break-Down algorithm is implemented in the iBreakDown package

https://github.com/ModelOriented/iBreakDown**. All new features will be added to the iBreakDown.

## Installation

Install from CRAN

```

install.packages("breakDown")

```

Install from GitHub

```

devtools::install_github("pbiecek/breakDown")

```

## Cheatsheets

![Cheatsheet](https://raw.githubusercontent.com/pbiecek/breakDown/master/cheatsheets/breakDownCheatsheet.png)

## Example for lm model

Get data with [archivist](https://github.com/pbiecek/archivist)

* broken object: `archivist::aread("pbiecek/breakDown/arepo/81c5be568d4db2ec795dedcb5d7d6599")`

* the plot: `archivist::aread("pbiecek/breakDown/arepo/7b40949a0fdf9c22780454581d4b556e")`

The R code

```{r}

library(breakDown)

url <- 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv'

wine <- read.table(url, header = T, sep=";")

head(wine, 3)

##   fixed.acidity volatile.acidity citric.acid residual.sugar chlorides free.sulfur.dioxide total.sulfur.dioxide density   pH

## 1           7.0             0.27        0.36           20.7     0.045                  45                  170  1.0010 3.00

## 2           6.3             0.30        0.34            1.6     0.049                  14                  132  0.9940 3.30

## 3           8.1             0.28        0.40            6.9     0.050                  30                   97  0.9951 3.26

##   sulphates alcohol quality

## 1      0.45     8.8       6

## 2      0.49     9.5       6

## 3      0.44    10.1       6

model <- lm(quality ~ fixed.acidity + volatile.acidity + citric.acid + residual.sugar + chlorides + free.sulfur.dioxide + total.sulfur.dioxide + density + pH + sulphates + alcohol,

               data = wine)

new_observation <- wine[1,]

br <- broken(model, new_observation)

br

##                            contribution

## (Intercept)                     5.90000

## residual.sugar = 20.7           1.20000

## density = 1.001                -1.00000

## alcohol = 8.8                  -0.33000

## pH = 3                         -0.13000

## free.sulfur.dioxide = 45        0.03600

## sulphates = 0.45               -0.02500

## volatile.acidity = 0.27         0.01500

## fixed.acidity = 7               0.00950

## total.sulfur.dioxide = 170     -0.00900

## citric.acid = 0.36              0.00057

## chlorides = 0.045               0.00019

## final_prognosis                 5.60000

plot(br)

```

![plot for lm model](misc/broken_lm.png)