Types of Model Interpretability
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Understanding Global Interpretability
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Today, we are going to dive into the first type of model interpretability: global interpretability. Can anyone explain what this means?
Is it about understanding how the entire model works?
Exactly! Global interpretability helps us understand the model's overall behavior. A common method for achieving this is feature importance ranking. Can anyone tell me what that means?
It ranks features by how much they contribute to the predictions?
Right! It helps us see which features impact the predictions across the entire dataset. Great job!
Understanding Local Interpretability
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Now, letβs move on to local interpretability. Can someone explain what it entails?
Is it about understanding why a specific prediction was made?
Correct! Local interpretability helps explore the reasoning behind individual predictions. Can you think of an example of a question we might ask?
Like, why did the model predict this patient will have a disease?
Exactly! Understanding the specific factors that contributed to that prediction is vital for trust.
Intrinsic vs Post-Hoc Interpretability
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Next, let's identify the difference between intrinsic and post-hoc interpretability. What do you think intrinsic interpretability means?
Does it mean the model is designed to be interpretable from the start?
Spot on! Intrinsically interpretable models, like linear regression, are understandable without additional tools. What about post-hoc interpretability?
That's when you use methods after the model is trained to explain it, right?
Exactly. Tools like LIME and SHAP fall into this category, helping us clarify how complex models operate.
Introduction & Overview
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Quick Overview
Standard
The section discusses the two primary types of interpretabilityβglobal and localβalong with their nuances. It distinguishes between intrinsic interpretability, which refers to models that are inherently understandable, and post-hoc methods that provide explanations after model training, using tools like SHAP and LIME as examples.
Detailed
Types of Model Interpretability
The need for interpretability in AI models is essential for transparency and trust. This section highlights the different types of interpretability, which can be broadly categorized as:
- Global Interpretability: Refers to understanding the overall behavior of the model. An example is feature importance ranking, where we assess how each feature contributes to predictions made by the model across multiple instances.
- Local Interpretability: Focuses on understanding specific predictions made by the model rather than the overall behavior. An example could be asking Why did the model predict X for Y? This type of interpretability seeks to provide context around individual outputs.
- Intrinsic Interpretability: Involves models that are designed to be interpretable intrinsically, like linear regression or decision trees, where coefficients and decision paths can be easily understood.
- Post-Hoc Interpretability: Entails methods applied after training the model to explain its behavior, such as techniques like LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and Partial Dependence Plots (PDP).
Understanding these types helps to evaluate the trade-offs between interpretability and model performance, especially in contexts where explainability is a legal or ethical requirement.
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Global Interpretability
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Chapter Content
Global: Understanding model behavior overall
Example: Feature importance ranking
Detailed Explanation
Global interpretability refers to understanding how a model behaves as a whole. This means looking at how every feature in the model contributes to the decisions made across all predictions. An example of global interpretability is feature importance ranking, where we can see which features are most influential in shaping the model's predictions. For instance, in a model predicting house prices, the size of the house might have a high importance ranking, showing that it is a key factor.
Examples & Analogies
Think of global interpretability like understanding how a recipe works. If you're making a cake, knowing that flour and sugar are the most important ingredients can help you see why the cake turns out a certain way. Just as you would look at the overall recipe to understand the cake, you look at feature importance to understand the model.
Local Interpretability
Chapter 2 of 4
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Chapter Content
Local: Explaining a specific prediction
Example: Why did the model predict X for Y?
Detailed Explanation
Local interpretability focuses on understanding why a model made a specific prediction for a particular instance. For example, if we have a model that predicts whether a loan applicant will be approved, local interpretability would help us explain why the model predicted approval for one applicant versus denial for another. This allows users to grasp what contributed to that specific decision.
Examples & Analogies
Imagine you are a teacher and want to understand why a student received a particular grade on an assignment. You analyze their answers and see that the student excelled in certain areas but struggled in others. This specific understanding corresponds to local interpretability, where you look closely at individual performance rather than overall class performance.
Intrinsic Interpretability
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Chapter Content
Intrinsic: Built-in interpretability (e.g., decision trees)
Example: Coefficients in linear regression
Detailed Explanation
Intrinsic interpretability refers to models that are inherently understandable due to their structure. For example, decision trees provide clear pathways for predictions, as you can trace back how a decision was made through a series of yes/no questions. Additionally, linear regression models display their relationships between features and outcomes through coefficients, making it easy to see the impact of each feature.
Examples & Analogies
Think of intrinsic interpretability like a simple map of a city. A straight road system that clearly lays out routes makes it easy to navigate. Similarly, a decision tree offers a clear path of understanding how the outcome is reached, just as a good map helps you find your way without confusion.
Post-Hoc Interpretability
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Chapter Content
Post-Hoc: Explanation after training
Example: LIME, SHAP, Partial Dependence Plots
Detailed Explanation
Post-hoc interpretability involves analyzing a model's predictions after the model has been trained. This approach uses various techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to generate explanations for how decisions were arrived at. For instance, these methods can help break down and attribute contributions of different features in a model's prediction, allowing for insights even if the model itself isn't interpretable.
Examples & Analogies
Consider a detective analyzing a case after it has been solved. They look at all the evidence and clues to explain how the conclusion was reached. Similarly, post-hoc interpretability works in reverse, examining a model's decisions after it has been trained to understand why certain predictions were made.
Key Concepts
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Global Interpretability: Overall understanding of model behavior.
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Local Interpretability: Insight into individual predictions.
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Intrinsic Interpretability: Built-in information from simple models.
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Post-Hoc Interpretability: Explanations after model training.
Examples & Applications
An example of global interpretability is ranking the importance of features in determining their impact on model predictions.
Local interpretability can be illustrated by analyzing why a model predicted a certain outcome for an individual case, like diagnosing a disease.
Memory Aids
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Rhymes
For global view, everything's fair,
Stories
Imagine a detective (Intrinsic) naturally uncovering clues to solve a case (model predictions), while a team of analysts (Post-Hoc) reviews the detective's notes later to ensure nothing was missed.
Memory Tools
Remember GLIP:
Acronyms
To recall the types, think of G.L.I.P - where G is for Global, L for Local, I for Intrinsic, P for Post-Hoc.
Flash Cards
Glossary
- Global Interpretability
Understanding the overall behavior and decision-making process of a model across all predictions.
- Local Interpretability
Understanding the reasoning behind specific predictions made by a model.
- Intrinsic Interpretability
Models that are naturally interpretable due to their simple structure, such as linear regression.
- PostHoc Interpretability
Techniques applied after model training to clarify how a model makes decisions.
- Feature Importance Ranking
Method to evaluate and rank the contribution of each feature to the model's predictions.
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