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Local surrogate models are interpretable models that are used to explain individual predictions of black-box machine learning models. Local interpretable model-agnostic explanations (LIME) is a paper in which the authors propose a concrete implementation of local surrogate models. Surrogate models are trained to approximate the predictions of the underlying black-box model. Instead of training a global surrogate model, LIME focuses on training local surrogate models to explain individual predictions.

The idea is quite intuitive. First, forget about the training data and imagine you only have the black-box model where you can input data points and get the model's predictions. You can probe the box as often as you want. Your goal is to understand why the machine learning model made a specific prediction. LIME tests what happens to the predictions when you give variations of your data into the machine learning model. LIME generates a new dataset consisting of permuted samples and the corresponding predictions of the black-box model.

Intro(0:00)
Import libraries(0:56)
Loading Data(1:55)
Data processing(4:43)
Random Forest(8:36)
Lime(9:48)
Outro(16:34)

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