Machine Learning | Multi Label Classification

Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem, there is no constraint on how many of the classes the instance can be assigned to. #MachineLearning #MultiLabelClassification
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[@RanjiRaj18]

For notes👉 github.com/ranjiGT/ML-latex-amendments

[@20060802Lin]

Thank you so much!! I have looked at many resources to understand how to tackle multilabel problems, this is the best one I found! It's so helpful that you lay out the big picture and how they tie together!!

[@RanjiRaj18]

Happy that you found value of my work 🙂

[@ebtihal_m9411]

Thank you so much for this informative video

[@TechResearch05]

Best video for multilabel classification. Thank you

[@josejoseramirezcastro8662]

Thank you very much for clarify the taxonomy of classification problem.

[@katesithole8629]

You really simplified the concept for me. Thank you Sir.

[@RanjiRaj18]

Happy to help

[@reednaidoo1788]

Great examples

[@rohitbansal7689]

thank you

[@NripaEmpowerthroughknowledge]

very nice , well explained

[@benjaminwiner6220]

This is wonderful

[@trilokigupta821]

Great Explanation

[@beddahanane9048]

where we classify the neural network NN or Deep NN in the multi label classification methods ? and is there any relation between them

[@RanjiRaj18]

It depends on your problem at hand

[@rajahossain2745]

Want more on discussion on Multilevel classification...
You can help me recommending books .

[@rfx153]

i am trying to do a multilabel classification, with 336 inputs and 168 outputs.
this data is binary only 0 or 1.
i have been researched about some methods and i conclued that binary relevance or one vs rest may be the best ways.
But i am not sure because i am fresh in machine learning/deep learning subject.
Do you have a tip for me?

[@RanjiRaj18]

You can try both of the methods and decide upon which yields good results

[@rfx153]

@@RanjiRaj18 I'm gonna try, Thanks man.

[@dianahoefels6379]

Hi Ranji Raj, what if we have a multi-class multi label text classification, what algorithms can we apply to solve the problem. Text documents comprise a number of classes and each class can be tagged with one label. Thank you.

[@RanjiRaj18]

You can use a neural network model problem for your use case with softmax activation at the output layer!

[@dianahoefels6379]

@@RanjiRaj18 Thank you for your response. l am supposed to try out other algorithms and not use neural networks and I am a bit confused and wonder if any of the algorithms described by you in this video can be employed to solve it. Thanks again!

[@m4meeran]

Thanks for the explanation Ranji. What if we have a huge number of label set. Will these methods helps? Assume we have 50k trained attributes for that 1800 labels we have. Will these methods works?

[@RanjiRaj18]

You have to make use of some stream processing algorithms combined with these techniques; possibly making use of some libaries like *Faust* or *multiflow* which can be scalable for any number of attributes or observations. Thank you for your time.

[@m4meeran]

@@RanjiRaj18 Cheers for replying. And thanks alot for suggesting solutions. Will try these libraries.❤️

[@SaptarsiGoswami]

Nice but why would call naive bayes to be binary classifier?

[@RanjiRaj18]

Because it classifies the output samples either in 0/1 , True/ False, Yes/No, +/- etc..

[@debojitmandal8670]

hi can you code and show us please

[@BenKHighlights]

Thanks daddy :P🐔