![]() ![]() The data in this sample can be modeled by a class like HousingPriceData and loaded into an IDataView. The features in the dataset being used for this sample are in columns 1-12. The larger the change, the more important that feature is.Īdditionally, by highlighting the most important features, model builders can focus on using a subset of more meaningful features which can potentially reduce noise and training time. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest decreases. PFI is a technique used to explain classification and regression models that is inspired by Breiman's Random Forests paper (see section 10). Various techniques are used to explain models, one of which is PFI. Therefore the higher the level of explainability in a model, the greater confidence healthcare professionals have to accept or reject the decisions made by the model. Providing the right diagnosis could make a great difference on whether a patient has a speedy recovery or not. For example, if diagnoses are made by a machine learning model, healthcare professionals need a way to look into the factors that went into making that diagnoses. As machine learning is introduced into more aspects of everyday life such as healthcare, it's of utmost importance to understand why a machine learning model makes the decisions it does. The intermediate steps or interactions among the features that influence the output are rarely understood. Machine learning models are often thought of as opaque boxes that take inputs and generate an output. PFI gives the relative contribution each feature makes to a prediction. Using Permutation Feature Importance (PFI), learn how to interpret ML.NET machine learning model predictions. ![]()
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