LIME(Local Interpretable Model-Agnostic Explanations) in XAI with an example in Python
The goal of the developing research area known as Explainable Artificial Intelligence (XAI) is to make sophisticated machine learning models transparent and understandable. LIME (Local Interpretable Model-Agnostic Explanations) is one of the methods most frequently employed in XAI.
LIME is a method that gives regional justifications for predictions provided by sophisticated machine learning models. The basic goal of LIME is to give humans, including those who are not experts in machine learning, interpretable explanations that are simple to understand.
In order to approximate the original model’s predictions within a constrained, narrow area of the input space, LIME builds a more straightforward, interpretable model.
The significance of each input characteristic for the prediction in that area is then determined using this local model to produce explanations in the form of feature weights.
LIME has been used to explain sophisticated machine learning models in a number of fields, including healthcare, computer vision, and natural language processing. It has been demonstrated to be successful in raising the level of confidence in and comprehension of these models as well as in spotting potential biases or mistakes.
LIME is an effective tool for XAI as a whole since it helps to close the communication gap between sophisticated machine learning models and human comprehension.
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