Open Source Project

ml-calibration

relplot is a toolkit for measuring calibration and plotting reliability diagrams.

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The relplot toolkit is designed as a resource for those who are working with predictive models and are interested in evaluating the calibration of these models. Calibration, in the context of predictive models, refers to how well the predicted probabilities of outcomes match the actual outcomes. In other words, a well-calibrated model’s predictions should reflect true probabilities. For instance, if a model predicts an event with 70% probability, we would expect that event to occur approximately 70% of the time in the real world if the model is well-calibrated.

The primary feature of relplot is its capability to create reliability diagrams, which are graphical representations that compare the predicted probabilities to the actual outcomes. These diagrams are crucial for visualizing the performance and reliability of a model, as they clearly illustrate any discrepancies between predicted probabilities and actual outcomes. By plotting these diagrams, users can easily identify whether a model tends to be overconfident (predicting probabilities higher than reality) or underconfident (predicting probabilities lower than reality) in its predictions.

Moreover, relplot provides various visual methods, beyond just reliability diagrams, to aid in the detailed analysis of a model’s calibration. These methods can help users to not only assess the current state of model calibration but also to pinpoint specific areas where the model might be improved to enhance its predictive confidence and reliability. Through its suite of visualization tools, relplot serves as a valuable asset for researchers, data scientists, and analysts who aim to improve the accuracy and trustworthiness of their predictive models.

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