Machine Learning Tutorial Python - 14: Naive Bayes Classifier Algorithm Part 1

Does we don't need to drop one dummy column?
Dose the dummy variable trap only for linead_mode?

can't the sex column be simplified by LabelEncoding??

That was the best ever tutorial I watched about naive bayes.... Thank you so much ❤

thanks a lot.. can i ask to you, what if i want to show xtest result after tf-idf sir? I have tried only with the xtest code but the results are not as desired

by saying male as a feature are u sure u did not confuse it with sex. I thought sex would be the feature with values such as male, female. Unless you meant male as a feature taking on values yes or no?

I guess you could've dropped from your dataset one of the columns 'male' and 'female', since they contain the same information.

EXPLAIN IN SIMPLE TERMS ......?? I DONT UNDERSTAND YOUR VIDEOS

thanks

I have a small query here. Why did we not drop the either female/male column after one hot encoding to avoid dummy variable trap?

this is a really good example explanation

Very well explained sir! Thanks :-)

I have question, why you did not drop either fenale or male column? In your previous tutorials, you said one column should be dropped if converting using dummy. Thanks...

How P(diamond/queen) is 1/4
Can somebody explain to me?

Sir plz explain when to take the mean median mode for null values .....

thank you bhai for the explanation

Simplicity is the ultimate sophistication. You are amazing!

what a gift of explanation!!!

How the fuck can I get the dataset