{"action":"created","comment":{"url":"https://api.github.com/repos/dotnet/machinelearning/pulls/comments/236858655","pull_request_review_id":179024650,"id":236858655,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIzNjg1ODY1NQ==","diff_hunk":"@@ -12,9 +12,21 @@ namespace Microsoft.ML\n     public static class PermutationFeatureImportanceExtensions\n     {\n         /// <summary>\n-        /// Permutation Feature Importance is a technique that calculates how much each feature 'matters' to the predictions.\n-        /// Namely, how much the model's predictions will change if we randomly permute the values of one feature across the evaluation set.\n-        /// If the quality doesn't change much, this feature is not very important. If the quality drops drastically, this was a really important feature.\n+        /// Permutation feature importance (PFI) is a technique to determine the global importance features in a trained\n+        /// machine learning model. PFI works by taking a labeled dataset, and then, going feature by feature, the values\n+        /// for that feature are permuted, and the resulting change in the metric values for the task is computed. The\n+        /// larger the change in the evaluation metric, the more important the feature is to the model. This is a simple\n+        /// feature importance scheme motivated by Breiman in his Random Forest paper, in section 10\n+        /// (Breiman. \"Random Forests.\" Machine Learning, 2001.) The advantage of the PFI method is that it is model\n+        /// agnostic -- it works with any model that can be evaluated -- and it can use any dataset, not just the training\n+        /// set, to compute feature importance metrics.\n+        /// /// 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the XML docs of `PermutationFeatureImportance` to include more details, a reference to the original Breiman paper, and a link to the new sample.\r\n\r\nFixes #1732 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