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Interpretability: Difference between revisions
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Interpretability in machine learning encompasses several distinct categories, such as: | Interpretability in machine learning encompasses several distinct categories, such as: | ||
#[[Global interpretability]]: This refers to an overall comprehension of a model's behavior and decision-making process. It takes into account predictions as a whole as well as relationships between [[input]]s and [[output]]s. | |||
#[[Local interpretability]]: This refers to deciphering individual predictions made by a model and the factors that influence them. It seeks to comprehend why one particular prediction was made for any given instance. | |||
#[[Model-specific interpretability]]: This refers to the interpretability of a particular model type, such as [[decision tree]]s, [[linear regression]] or [[neural network]]s. It involves understanding how that particular model works and how its predictions are made. | |||
==Interpretability Techniques== | ==Interpretability Techniques== | ||
Interpretability in machine learning can be accomplished through several techniques, such as: | Interpretability in machine learning can be accomplished through several techniques, such as: | ||
#[[Feature importance]]: This technique involves ranking the features used by the model according to their importance in making predictions. Generally, the most crucial features have the greatest influence on model outputs. | |||
#[[Model visualization]]: This technique involves visualizing a model's structure and decision-making process. For instance, decision trees can be represented as a tree structure, with each [[node]] representing a decision and each branch representing possible outcomes. | |||
#[[Partial dependence plot]]s: This technique illustrates the relationship between model predictions and individual features, while holding all other features constant. This helps us comprehend how the model takes into account each feature when making its predictions. | |||
#[[Counterfactual analysis]]: This technique involves comparing the model's predictions for a given instance with what would have happened if certain features had been altered. Doing so helps us gain insight into what factors are causing the model's predictions to differ. | |||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Interpretability in machine learning is like watching a magician perform a trick. | Interpretability in machine learning is like watching a magician perform a trick. We want to understand how the trick works. Just as when a machine learning model makes a prediction, we want to understand its process - what factors it considers and how it makes its decisions. | ||
To make machine learning models simpler to comprehend, we employ techniques such as feature importance, model visualization and partial dependence plots. These visual aids demonstrate how the model makes its predictions and which factors it takes into account. | To make machine learning models simpler to comprehend, we employ techniques such as feature importance, model visualization and partial dependence plots. These visual aids demonstrate how the model makes its predictions and which factors it takes into account. | ||
Interpretability in machine learning helps us comprehend its inner workings! | Interpretability in machine learning helps us comprehend its inner workings! | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] | ||