The main idea of LIME is to approximate the black box machine learning model locally (per sample) using a surrogate model. The surrogate model is a simple interpretable model.
Overview of the method :
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Preprocess the image: Convert the image into a format suitable for processing. This may involve resizing, normalizing pixel values, and converting color channels if necessary.
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Select the image to be explained: Choose the specific image you want to explain. It could be an image from your dataset or any image of interest.
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Choose a prediction model: Select a machine learning model that was trained on a similar task or dataset. This model will be used to generate predictions for the image.
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Generate perturbed samples: Create perturbed versions of the image by applying random transformations or perturbations. These perturbations should be applied to different parts of the image to understand their effect on the predictions.
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Obtain predictions: Pass the perturbed images through the prediction model and collect the corresponding predictions for each perturbed sample.
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Generate interpretable features: Convert the perturbed images and their predictions into interpretable features that can be understood by humans. This could involve extracting relevant features or transforming them into a more interpretable representation.
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Train an interpretable model: Use the interpretable features and the corresponding predictions to train an interpretable model, such as a linear model or decision tree. This model should approximate the behavior of the prediction model on the interpretable features.
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Explain the image prediction: Use the trained interpretable model to explain the prediction of the original image. This can be done by analyzing the contributions of different interpretable features to the final prediction.
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Visualize the explanations: Present the explanations in a visually appealing and intuitive format. This could involve overlaying heatmaps on the original image to highlight the important regions or generating textual explanations describing the contributing features.