2. Solved Example Naive Bayes Classifier to classify New Instance | Species Example by Mahesh Huddar

[@jhvhj6429]

One tiny mistake. The correct values are: P(Tall | M) = 1/4 P(Short | M) = 3/4 In your video, you have written the opposite. Other than that, everything is perfect. Fantastic video! Thank you.

[@AbhishekKumar-ff9vg]

Yes and that's why even the v( M ) also coming differently.

[@AbhishekKumar-ff9vg]

@@MaheshHuddar can you please solve these problem once again sir after removing the problem . I am having doubt.

[@dianatetteh5070]

True

[@razanalolimat]

yes true there is a mistake , thank you for editing it.

[@praveenps2000]

even I noticed this just now ..have 3rd internals tomorrow

[@madhurac6292]

There is a small mistake in calculating probabilities for height column v(M) = 0.0039 v(H) = 0.0468 v(H) > v(M) Therefore, species = H

[@praveenps2000]

noice got the same answer

[@kevinkenny871]

thank ouuuuu

[@venkateshboda7531]

This is correct only know....?

[@fizamustafa1447]

Same answer as mine

[@megha24nov]

my V(H) = .09

[@vedantnaik7027]

The value of tall/m is 1/4 not 3/4 plz correct it plz

[@bismithaa7631]

Yess

[@harikishore8875]

Yes

[@TamilSelvi.P]

Yes sir it is 1/4

[@bharatkumarnaik54]

Yes

[@Aliyaaugust]

I think short /m & tall/m got switched

[@rohitkonnur5376]

One small mistake. The correct values are: P(Tall | M) = 1/4 P(Short | M) = 3/4 And final probabilities are: P(M/new instance)= 0.003906 P(H/new instance)=0.046875 Other than that, everything is perfect. Fantastic video! Thank you.

[@MaheshHuddar]

Thank You Do like share and subscribe

[@abdulhameedmalik4299]

this is a best video in shot time when the video have time more than 10 minutes people loose their interesting. I appreciate your hard work. because a best product come after a hard work

[@MaheshHuddar]

Thank You Do like share and subscribe

[@Neelima-Neelu]

The way you are teaching really amazing 👏👏👏👏its so easy to understand

[@MaheshHuddar]

Thank You Do like share and subscribe

[@javeriyafirdose6928]

Thank you sooo much sir you made it sooo easy by ur explanation!!

[@fatimaiqra2169]

Most content covered in very short very...awesome lecture ...thanks

[@MaheshHuddar]

You're welcome! Do like share and subscribe

[@AbhishekK063]

Tall and short probability is wrong, Sir. Anyhow, it helped me with the concept Thank you

[@ezhilarasis9690]

yes

[@umairghani4334]

Respected Sir, Conditional Probability for Tall regarding M is 1/4 not 3/4 ...

[@devika1338]

Informative, thankyou 😊

[@ammarnusair]

Thank you for the explanation

[@MuhammadBilal-kz9tm]

dude u have to do normalization as well.... i think thats the part of naive bayes theorem

[@MaheshHuddar]

Normalisation is required in naive Bayes theorem but not in naive Bayes classifier

[@fintech1378]

can you have video on numeral examples about CNN? like calculating trainable parameters and output layers etc? and also word embedding

[@narendrasappa]

Thank You

[@MaheshHuddar]

Welcome Do like share and subscribe

[@hiccup2op]

Thank you sir

[@MaheshHuddar]

Welcome Do like share and subscribe

[@bhupendra382]

Thanks sir

[@neamul3676]

Neural networks aro video saii. Sir

[@shankarchalwadi247]

benki ett sir ,from 3 final year students

[@gamerman7248]

@@MaheshHuddar sir for tall only 3 values is there means short 5 and tall 3

[@vennilat7786]

Thanks

[@MaheshHuddar]

Welcome Do like share and subscribe

[@banu1690]

thank u sir

[@MaheshHuddar]

Welcome Do like share and subscribe

[@nallarasukrishnan4364]

What are M and H?

[@MaheshHuddar]

Target classes

[@nallarasukrishnan4364]

@@MaheshHuddar Thank you. But I had asked the English meaning or expansion to M nd H. M means Medium, nd H means High?

[@drmaziz72]

How we find new instances

[@MaheshHuddar]

New instance is given as a part of problem

[@NajeebUllah-i4u]

❤❤❤❤

[@MaheshHuddar]

Thank You Do like share and subscribe