Improvement in Naive Bayes model

[KrishnenduDakshi2002]

A Naive Bayes model is already present in the classification notebook.

Confusion Matrix True positive (TP): Prediction is +ve and X is Cancerous, we want that True negative (TN): Prediction is -ve and X is healthy, we want that too False positive (FP): Prediction is +ve and X is healthy, false alarm, bad False negative (FN): Prediction is -ve and X is Cancerous, the worst

Requested Feature

improvement in model prediction ( less False positive and less false negative(need more attention and improvement)) Cross validation or hyper parameter tuning can be done to improve the model.

About metrics

Choose Recall if the idea of false positives is far better than false negatives, in other words, if the occurrence of false negatives is unaccepted/intolerable, that you’d rather get some extra false positives(false alarms) over saving some false negatives, like in our cancerous example.

[srini047]

I would like to take up the issue.

[KrishnenduDakshi2002]

Hi @srini047 , I assigned the issue. Go ahead.