8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP

For data scientists, a regression summary might be all that's needed to understand a linear model. However, when explaining these models to a non-technical audience, it’s crucial to employ more digestible visual explanations. These 8 methods not only make linear regression more accessible but also enrich your analytical storytelling, making your findings resonate with any audience.
We understand how to interpret:
- Residual plots
- Correlation heatmaps
- Weight plots
- Effect plot
- Mean effect plot
- Individual effect plot
- Trend effect plot
- SHAP values for linear models
🚀 Free Course 🚀
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XAI course: adataodyssey.com/courses/xai-...
SHAP course: adataodyssey.com/courses/shap...
🚀 Companion article with link to code (no-paywall link): 🚀
medium.com/towards-data-scien...
🚀 Useful playlists 🚀
XAI: • Explainable AI (XAI)
SHAP: • SHAP
Algorithm fairness: • Algorithm Fairness
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Medium: / conorosullyds
Threads: www.threads.net/@conorosullyds
Twitter: / conorosullyds
Website: adataodyssey.com/
🚀 Chapters 🚀
00:00 Introduction
0:37 Credit risk dataset
2:20 Residual plots
4:02 Correlation heatmaps
5:01 Weight plots
6:03 Effect plot
8:17 Mean effect plot
9:11 Individual effect plot
10:37 Trend effect plot
12:06 SHAP values

[@adataodyssey]

🚀 Free Course 🚀
Signup here: mailchi.mp/40909011987b/signup
XAI course: adataodyssey.com/courses/xai-with-python/
SHAP course: adataodyssey.com/courses/shap-with-python/

[@franciscoletelier5598]

Great! always clear

[@adataodyssey]

That’s my goal!

[@shazajmal9695]

Thanks Bruh! Great Content! Would be happy if you upload a video comparing Shap with LIME and Integrated Gradients. Its a hot topic rn in data science interviews.

[@adataodyssey]

Thanks for the suggestion! Would this be w.r.t. computer vision models and deep learning?