Thank you very much for help... One of the best videos on UA-cam for this numerical on naive Bayes classification algorithm..cleared the concept.. Thanks again
you all probably dont give a shit but does any of you know a trick to log back into an instagram account..? I somehow forgot my login password. I would love any tricks you can offer me.
Thank you very much now I can solve any type of problems of naive bayes classification 😊....your videos are really very helpful.... keep uploading more videos.....
The formula for Normal/Gaussian Distribution is wrong; It should be exp-((A i - mew ij) ^2)/2 * sigma^2). Please stick to the standard formula and don't make own changes
Very good explanation but frequency table was not calculated ..!! Class teacher taught us in discrete values not in case of salary ..!! By the way very well explanation 🤎
Hi, Laplace smoothing to be done to avoid the zero probability calculation. Appreciate if you can make a video on laplace smoothing in Naive bayes with a solved example. Nevertheless, the video is really helpful.
Shouldnt it be P(X/class=No)=P(no)*P(Refund=No/class=no)*P(Married/class=No)*P(income=120k/class=No). You missed out P(No). Similarly You missed P(yes) for P(X/class=yes).
Best explanation on UA-cam to solve numerical on naive Bayes classification algorithm
Thank you very much for help... One of the best videos on UA-cam for this numerical on naive Bayes classification algorithm..cleared the concept.. Thanks again
you all probably dont give a shit but does any of you know a trick to log back into an instagram account..?
I somehow forgot my login password. I would love any tricks you can offer me.
You are also for him . Enter my channel and do subscribe plz. I will make tutorial like this. For beginner you're support will help me.
there is alot video talking about naive bayes but this is the one and only explanation naive bayes with numeric data, thank you
Thank you very much now I can solve any type of problems of naive bayes classification 😊....your videos are really very helpful.... keep uploading more videos.....
Thank you bro, now I can solve any type of problems of naive bayes classification 😊
You are also for him . Enter my channel and do subscribe plz. I will make tutorial like this. For beginner you're support will help me.
That's a clear and crisp explanation...thanks for uploading...it made the things easy for me....thanks mr. Yogesh Murumkar
You are also for him . Enter my channel and do subscribe plz. I will make tutorial like this. For beginner you're support will help me.
thats simply an awesome explanation....thanks a lot for uploading....best video to solve numerical on naive bayes classification algorithm
This is perfect!!!10 out of 10.... amazing simplicity!!!!!it helped me really a lot.... thank you!!!
You are also for him . Enter my channel and do subscribe plz. I will make tutorial like this. For beginner you're support will help me.
Tq
this is the best explanation of N.B. out of many that I have seen! great job
Thank you sir....... Simple explanation.....
Really appreciated... Best
best explanation! I understood everything. It will surely help me in my exams. Thank u so much !!
The formula for Normal/Gaussian Distribution is wrong; It should be exp-((A i - mew ij) ^2)/2 * sigma^2). Please stick to the standard formula and don't make own changes
Excellent
Many years!!! Wish u good health!! Save my life...
awesome.....
Awesome man.... excellent
Thank you for such a great lecture. It helps a lot to understand my course material.
You are also for him . Enter my channel and do subscribe plz. I will make tutorial like this. For beginner you're support will help me.
very nice explanation sir
Thank you sir...
Thank you so much sir!!! Great explanation
I am glad if it helped you...
Awesome explanation bro.
Good explanation!!
Your explain is pretty good! Thank you
Aapka hi sahara hai...
Awesome Explanation!
Great explanation! Thank you.
thanks sir, my homework is 100% same like this. hahaha thats make my day easier
clearly explained ...thank you so much
Perfect explanation, thanks a lot !
In calculation of normal distribution for “yes” from were did 10^(-9) came?
Very good explanation but frequency table was not calculated ..!!
Class teacher taught us in discrete values not in case of salary ..!!
By the way very well explanation 🤎
neatly explained
Huge thanks to u 🙏🙏🙏🙏🙏
I am glad if it helped you!!!
P(Income=120k|Class=Yes) = 0.145 not 1.2*(10^-9)
Sir please upload the video of impuritiy measure (Gini index and entropy)
sure
Thank you so much sir
Why has the formula for Normal distribution modified here? Isn't the formula (the front) * e ^ (X- u)**2 / 2 * pi (Sigma)**2 ???
what will happened if one of the samples does not exist like taxable amount = 80k
will be undefined but can we solve it or not !?
What if the income given in the instance is not 120k and some other value which is not present in the taxable amount column like 150k?
Hi,
Laplace smoothing to be done to avoid the zero probability calculation. Appreciate if you can make a video on laplace smoothing in Naive bayes with a solved example.
Nevertheless, the video is really helpful.
That's correct. Laplace smoothing should have been used in this example.
can you do a video on Maximal Frequent Item Set, Closed Frequent Item Set in data mining?
It will be really helpful to me.
sure Rohith...
@@yogeshmurumkar thank you 😊
super clarity
can you show how to construct a decision tree for the same dataset?
Excellent!!!!
sir ,
How to calculate the exponential term
In last which vlaue we consider maximum value or minimum
maximum
thank you so much i need desision tree continous attribute
thank you its help me
thanks a lot man
Sir I want ID3 problem in the same question
sure I will try to come with it.....
How we should take X value 🤔
How is P(A/D) = P(D/A) * P(A)/P(D) related to P(X/D) = P(A/D) * P(B/D) * P(C/D) ?
Sir I think you have calculated wrong contidional probability
And thanks for making video
dev माणूस 🍻
Shouldnt it be P(X/class=No)=P(no)*P(Refund=No/class=no)*P(Married/class=No)*P(income=120k/class=No). You missed out P(No).
Similarly You missed P(yes) for P(X/class=yes).
yes that's, what I was confused about
Great job. You deserve more views
You are supposed to handle zero probability error in the solution.
0.0072 is not the answer @ 22:02
I got it as 0.03928 for the normalization
nope. its correct. check again your calculation
@@NathanLAlvares I didn't understand the calculations what is done... can u do step by step
The formula for the normal distribution was wrong
Dude, it's not right to have a conditional probability of zero in the multiplication! That's why we use the Laplace smoothing. Read it up!
Kuch...
Jata long nhi hay
My prb has 2 columns of district value what to do?
This is all wrong
Don't be jealous man ...he is doing good job