Improvement in Naive Bayes model
Closed this issue · 2 comments
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.
I would like to take up the issue.
Hi @srini047 , I assigned the issue. Go ahead.