GitHub / CyprianFusi / Predicting-Heart-Disease-using-Logistic-Regression-Classification-Algorithm

With a precision of 86% and model's CAP curve showing an accuracy of 100%! This means it is capable of correctly predicting 100% of patients with a heart disease after processing 50% of the data. The model's performance is "Too Good to be True"! However, with Train accuracy = 86% and Test accuracy = 82%, there is no visible sign of overfitting.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyprianFusi%2FPredicting-Heart-Disease-using-Logistic-Regression-Classification-Algorithm

Topics: cap-curve, false-negative, false-positive, heart-disease, logistic-regression, precision, type-1-and-type-2-errors

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