6/30/2023 0 Comments Tensorflow permute mnistVisualize SHAP Values For Incorrect Predictions. Visualize SHAP Values For Correct Predictions.Explain Predictions Using SHAP Partition Explainer.Then, we have explained correct and incorrect predictions using SHAP python library.īelow, we have listed important sections of tutorial to give an overview of the material covered. SHAP - Explain Machine Learning Model Predictions using Game-Theoretic ApproachĪs a part of this tutorial, we have designed a simple CNN using keras and trained it with the Fashion MNIST dataset.Please check the below link if you want to refer to it. We have a starter tutorial on SHAP where we discuss how to use it for tabular (structured) datasets. We can then visualize these shap values using various visualizations to understand which features contributed to prediction. SHAP is a python library that generates shap values for predictions using a game-theoretic approach. There are many prediction interpretation libraries but as a part of this tutorial, we'll be using SHAP. If that is the case then we can be sure that our model has generalized better and actually learning features of cats and dogs. Let's say for example that we have an image classification task of predicting cat vs dog then the model should look at pixels of face and body of cat/dog to predict class, not the background pixels of images should be used to make a decision. We need to understand that the models that are giving such high accuracy are predicting results based on data parts that they should use for prediction. SHAP Values for Image Classification Tasks (Keras) ¶ĭeep learning models like convolutional neural networks are giving quite good results at many computer vision tasks.
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