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---
title: "Explanatory Model Analysis Book Club"
author: "The Data Science Learning Community"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
documentclass: book
bibliography: book.bib
biblio-style: apalike
link-citations: yes
github-repo: r4ds/bookclub-ema
description: "This website is the product of the Data Science Learning Community's Explanatory Model Analysis Book Club."
---
# Welcome {-}
Welcome to the Explanatory Model Analysis book club!
This website is a companion for the book [_Explanatory Model Analysis_](https://ema.drwhy.ai/) by Przemyslaw Biecek and Tomasz Burzykowski (copyright 2021).
You can find this presentation at [dslc.io/ema](https://dslc.io/ema).
This website is being developed by the [Data Science Learning Community](https://dslc.io). Follow along, and [join the community](https://dslc.io/join) to participate.
This companion follows the [Data Science Learning Community Code of Conduct](https://dslc.io/conduct).
## Book club meetings {-}
- Each week, a volunteer will present a chapter from the book after assigning it to themselves in the [Volunteers Spreadsheet](https://docs.google.com/spreadsheets/d/1vGnIw4y2QydGYPjUrPJlToawhLpwDpRPj-yjPl2KZIQ/edit#gid=0)
- Presentations:
- Review of material
- Questions you have
- Maybe live demo
- More information about how to present is available at [github.com/r4ds/bookclub-ema](https://github.com/r4ds/bookclub-ema).
- Presentations will be recorded, and will be available on the [Data Science Learning Community YouTube Channel](https://dslc.io/youtube).
## Pace {-}
- Meet ***every*** week except holidays, etc
- Meetings = **1 hour.**
- **Goal:** 1 chapter/week
- Ok to split overwhelming chapters
- Ok to combine short chapters
- If we need to **slow down** and discuss, **let me know**.
- Most likely someone has the same question
- We are all here to learn
## Introductions {-}
If you feel comfortable sharing, unmute or raise your hand!
- **Who** are you?
- **Where** are you joining from?
- **Previous clubs?** (DSLC or other)
- **How long** have you been using R or Python?
- **What** are you most looking forward to learning?
## Before reading this book {-}
- You should know how to use R or Python
[R for Data Science](https://r4ds.hadley.nz/) | [Python for Data Analysis](https://wesmckinney.com/book/)
:-------------------------:|:-------------------------:
 | 
## Before reading this book {-}
- You should know how to train, re-sample, evaluate, and tune models by using packages like:
- `CatBoost`
- `LightGBM`
- `XGBoost`
- `Keras`
- `H2O`
- `Tidymodels` or `Scikit-learn`
{width=63.33% height=95% fig-align="center"}
## What will we learn? {-}
- How to determine which **explanatory variables** affect a model's prediction for a **single observation**.
- Break-down plots
- Ceteris-paribus profiles
- Local-model approximations
- Shapley values;
- Techniques to examine predictive **models as a whole**.
- Partial-dependence plots
- Variable-importance plots
- **Charts** that can be used to present the key information in a quick way
- Tools and methods for **model comparison**
## What will we learn? {-}
{width=69.69697% height=95% fig-align="center"}