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Renato canova training pdf sheet
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If the prediction is greater than 0. 5 then the output is 1 else the output is 0 edict(X_test) y_pred =(y_pred>0. 5) Now is the moment of truth. we check the accuracy on the test dataset from trics import confusion_matrix cm = confusion_matrix(y_test, y_pred) print(cm) Confusion matrix total of true positive and true negative is 179 out 231 observations in the test dataset. so our accuracy for test dataset is around 78%. With the given inputs we can predict with a 78% accuracy if the person will have diabetes or not In the next post we will use K fold cross validation along with parameter tuning and how we can use dropout to handle overfitting. References:
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