Generative and Discriminative Classification | Generative and Discriminative Machine Learning

[@levmeyers]

You're the only person that explained this in a manner that allowed me to legitimately understand these topics. Rlly helping me out in my CIS class. Thanks a lot!

[@UnfoldDataScience]

Thanks a lot.

[@dhanushkannagovindaraju8069]

i am regretting now for joining college wasting lakhs of money to learn nothing..but 5 mins u made the concept easy to understand...Hats off sir..

[@yanzhenhuang9820]

You simply saved my life. Thanks!!!

[@fatimazohrabechlaghem7680]

Lovely video!! I needed this for my exam. Can you please try to answer the questions you asked at the end? here are my guesses: Which model will be effected by missing data: Descriminative Which model will need more data: Descriminative. Less data: generative which model will be effected by outliers: i guess both? which model will need more calculus: I think Descriminative which model will tend to overfit: descriminative as well. Please feel free to answer and correct with simple explanations as soon as you can as my exam is approaching. I very much appreciate this! new subscriber:)

[@UnfoldDataScience]

Good answers Fatima.

[@damikaanupama3788]

I think there should be some modifications: - Outliers have a greater impact on generative models due to the smaller amount of data points included. - Because generative models obtain the distribution of current data and examine it to the most likely distribution, they require more mathematics than discrete models.

[@Days_Gone]

Explanation was so good! Also the quiz at the end, wow! Nicely done

[@UnfoldDataScience]

Thanks Rishabh.

[@Mauricio-rg4lt]

Very clear explanation. I like the example and the visualization! I am a new subscriber!

[@amalkumar256]

Very helpful sir

[@kadourkadouri3505]

logit is definitely more prone to overfitting as it relies on more data to learn and there's a good probability that it will fit to noise

[@emizemani6958]

Great explanation! Simple and to the point. Thanks a lot! :)

[@UnfoldDataScience]

Thank you.

[@l.l.3609]

Appreciate this explanation! TY!

[@Aaron_Patrick_2004]

Nice Explanation Sir Thanks

[@ShivamTiwari-on2kl]

Outliers will affect only Discriminative right? or both? Both seems to be the right ans and my logic for it is that we already know that linear models see their curves affected by outliers while in the case of an algo like gaussian nb, the likelihood of an outlier happening will be very low for the given distribution and so that will bring the probability down. Can u please confirm if I am right or wrong?

[@shobhamourya8396]

Discrimivative models need more data therefore tend to be overfitted whereas Generative models built with less data may not generalize well with new data due to bias.

[@UnfoldDataScience]

Yes we can say like it Shobha.

[@gothams1195]

Bhai i like your videos, I wish you grow on youtube .

[@UnfoldDataScience]

Thanks Gotham.

[@aakashmittal8598]

Thanks for making us understand in such an easy way ✨🙏

[@UnfoldDataScience]

My pleasure 😊

[@swatisingh4041]

please make a video on Generative adversarial network on regression problem. There are so many GAN models for Images, but i couldn't find one for continuous values

[@Anthestudios]

Many thanks from Belgium!

[@UnfoldDataScience]

Thanks for watching.

[@che_sta]

Thank you! Awesome video, really great analogies and very clear.

[@UnfoldDataScience]

Glad it was helpful!

[@swethanandyala]

Thank you for your clear explanation Aman👍

[@UnfoldDataScience]

My pleasure

[@borna430]

What kind of background you need to get into Data science or AI concept?

[@UnfoldDataScience]

Statistics + Mathematics to start with

[@mouleshm210]

Hi sir, Can you take video on real-time A/B testing at the time of model deployment?

[@UnfoldDataScience]

ok

[@abhisheksaini5217]

Thank you Sir.. Nice explanation

[@UnfoldDataScience]

Welcome Abhishek.

[@arichandranr3626]

Hi, can you make video for HMM model for Time series dataset?

[@pavan.kumar.bb.b.7088]

Good explanation

[@anbesivam7686]

Hi Aman, I just started using Python. I am very basics. Please tell important functions that's very much needed for data scientist. Or tell where I can learn Python in advance level. Note: i am unemployed.

[@KairavLive]

Just go thru the Code with Harry KZbin channel.finish python.. rest will follow

[@borna430]

So if my eyes are closed and someone gives me a piece of fruit and ask to taste it and tell me what it is. That would be discriminative?

[@sandipansarkar9211]

finished watching

[@nikitasharma4957]

Sir please deep generative model pr vedio bnaiye

[@nikhielsingh748]

great video

[@UnfoldDataScience]

Thank you

[@billaspiel]

very good video , can you clarify below query the concept of generative mode is not clear in your example alien2 - compared features and did prediction alien1 - drew apple and banana and compared it with test sample and did prediction but to draw apple and banana we need to know its features correct then only you can draw it correctly so both models use features in the end to make prediction , so what difference is here ? how generative mode approach is different from discriminative ?

[@Julaiarvind]

Generative models don't draw features infact they understand distributions. Whenever a new query point comes, based on the probability, the class with highest probability will be assigned like probability of a mail P(spam) = 0.4 & p(ham) = 0.6. The query point gets assigned to ham class.

[@billaspiel]

@@Julaiarvind. Thanks but to build a distribution we use frequency of a particular feature so wats the difference .

[@sarkersunzidmahmud2875]

@@billaspiel I think it's like this, In generative, we use features to find the distribution of the data in the n-dimensional plane. For example distribution 1 is for apple and distribution2 is for banana. When a new point will come we will measure the probability of this new point on those two distribution. and in discriminative, we use features to make the prediction directly. What is the best prediction for y given these x? Here we use decision boundary, not the distribution. For example, if the width is 10, the height is 5, color is yellow then it's a Banana.

[@makkenamaryelizbeth3286]

Thank you

[@leelavathigarigipati3887]

The way of explanation is too good and the questions you asked, in the end, make me think deeply about what I understood. Thank you

[@UnfoldDataScience]

You are most welcome

[@beautyisinmind2163]

answer is Discriminative model will be effected by missing data.

[@UnfoldDataScience]

True, thank you.

[@osmaninci672]

0:45 he asked u back What is Fruit. 😂😂😂😂

[@UnfoldDataScience]

🤣🤣

[@AdityaAgarwal-v3b]

great video

[@UnfoldDataScience]

Thanks Aditya.