What is Explainable AI?

The field aimed at making machine learning models understandable to humans

A model could reject your application for a mortgage or diagnose you with cancer. These decisions have consequences. Serious consequences. Even if they are correct, we would expect an explanation. To get these we look to the field of explainable AI. 

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Both Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots are a popular explainable AI (XAI) method. They can visualise the relationships used by a machine learning model to make predictions. In this video, we will see how to apply the methods using Python. We will use the scikit-learn package and the PartialDependenceDisplay & partial_dependence functions. We will see that this allows us to easily visualise the plots including: - PDPs for individual features - 2-dimensional PDPs - Custom ICE Plots - ICE Plots for categorical features - ICE Plots for binary target variables 🚀 Free Course 🚀 Signup here: https://mailchi.mp/40909011987b/signup XAI course: https://adataodyssey.com/courses/xai-with-python/ SHAP course: https://adataodyssey.com/courses/shap-with-python/ 🚀 Companion article with link to code (no-paywall link): 🚀 https://towardsdatascience.com/the-ultimate-guide-to-pdps-and-ice-plots-4182885662aa?sk=31087acdb2a0dbcf44fcce459b643773 🚀 Useful playlists 🚀 XAI: https://www.youtube.com/playlist?list=PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4 SHAP: https://www.youtube.com/playlist?list=PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK Algorithm fairness: https://www.youtube.com/playlist?list=PLqDyyww9y-1Q0zWbng6vUOG1p3oReE2xS 🚀 Get in touch 🚀 Medium: https://conorosullyds.medium.com/ Threads: https://www.threads.net/@conorosullyds Twitter: https://twitter.com/conorosullyDS Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Introduction 00:51 Application with scikit-learn 02:21 Applying PDPs 08:22 Custom ICE Plot 09:48 2D PDPs 10:54 Categorical features 11:47 Binary target variables
PDPs and ICE Plots | Python Code | scikit-learn Package
Both Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots are used to understand and explain machine learning models. PDPs can tell us if a relationship between a model feature and target variable is linear, non-linear or if there is no relationship. Similarly, ICE plots are used to visualise interactions. Now, at first glance, these plots may look complicated. But you will see, they are actually constructed in a fairly intuitive way. In this video, we will: - Take you step-by-step through how PDPs and ICE plots are created. - Discuss what insight the explainable AI methods can give - And we will end by explaining the mathematics behind PDPs. 🚀 Free Course 🚀 Signup here: https://mailchi.mp/40909011987b/signup XAI course: https://adataodyssey.com/courses/xai-with-python/ SHAP course: https://adataodyssey.com/courses/shap-with-python/ 🚀 Companion article with link to code (no-paywall link): 🚀 https://towardsdatascience.com/the-ultimate-guide-to-pdps-and-ice-plots-4182885662aa?sk=31087acdb2a0dbcf44fcce459b643773 🚀 Useful playlists 🚀 XAI: https://www.youtube.com/playlist?list=PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4 SHAP: https://www.youtube.com/playlist?list=PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK Algorithm fairness: https://www.youtube.com/playlist?list=PLqDyyww9y-1Q0zWbng6vUOG1p3oReE2xS 🚀 Get in touch 🚀 Medium: https://conorosullyds.medium.com/ Threads: https://www.threads.net/@conorosullyds Twitter: https://twitter.com/conorosullyDS Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Introduction 01:31 Understanding PDPs 04:20 Visualising relationships with PDPs 06:58 Understanding ICE Plots 06:26 The maths
Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math
Feature importance scores are a collection of methods all used to answer one question: which machine learning model features have contributed the most to predictions in general? Amongst all these methods, permutation feature importance is the most popular. This is due to it’s intuitive calculation and because it can be applied to any machine learning model. Understanding PFI is also an important first step in understanding more complex explainable AI methods like SHAP, LIME and PDPs. In this video, we’re going to gain a deep understanding of PFI. To do this, we will use Python to calculate importance scores from scratch. We will also discuss the logic behind the method including why we permute, repeat and which metric to use. Really, we will be focussing on the P in PFI. Permutation is an important part of many model-agnostic methods. Taking time to understand this approach and its limitations will make those methods easier to understand. 🚀 Free Course 🚀 Signup here: https://mailchi.mp/40909011987b/signup XAI course: https://adataodyssey.com/courses/xai-with-python/ SHAP course: https://adataodyssey.com/courses/shap-with-python/ 🚀 Companion article with link to CODE (no-paywall link): 🚀 https://medium.com/towards-data-science/permutation-feature-importance-from-scratch-b8dcaceba9c2?sk=f8874fa7047188f62db6a24457102655 🚀 Useful playlists 🚀 XAI: https://www.youtube.com/playlist?list=PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4 SHAP: https://www.youtube.com/playlist?list=PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK Algorithm fairness: https://www.youtube.com/playlist?list=PLqDyyww9y-1Q0zWbng6vUOG1p3oReE2xS 🚀 Get in touch 🚀 Medium: https://conorosullyds.medium.com/ Threads: https://www.threads.net/@conorosullyds Twitter: https://twitter.com/conorosullyDS Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Introduction 01:15 PFI from scratch 06:54 The logic behind PFI 10:02 The limitations of permutation
Permutation Feature Importance from Scratch | Explanation & Python Code
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