Neural Networks, Logistic Regression, and Calibration

bration than a logistic regression model. In our view, the calibration of both models was problematic: the risks were underestimated for patients with low predictions ( < 8%) and overestimated for patients with high predictions (> 8%, fig. 4’). This pattern occurred with both methods, in the training set and especially in the validation set. We offer some comments about this study. First, a new element in the application of NNs was the

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