Answers.Body: <p>It's not accurate to say that classical machine learning implements only discriminative AI while deep learning implements both generative and discriminative AI. Both classical machine learning and deep learning have methods for both generative and discriminative tasks. For example, the classical <a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Relation_to_logistic_regression" rel="nofollow noreferrer">naive Bayes</a> for classification and some NLP tasks and GMMs used for clustering and density estimation are typically considered as a generative model.</p>
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  <p>In the case of discrete inputs (indicator or frequency features for discrete events), naive Bayes classifiers form a generative-discriminative pair with multinomial logistic regression classifiers: each naive Bayes classifier can be considered a way of fitting a probability model that optimizes the joint likelihood <span class="math-container">${p(C,\mathbf {x} )}$</span>, while logistic regression fits the same probability model to optimize the conditional <span class="math-container">$p(C\mid \mathbf {x} )$</span>.[17]... Discriminative classifiers have lower asymptotic error than generative ones; however, research by Ng and Jordan has shown that in some practical cases naive Bayes can outperform logistic regression because it reaches its asymptotic error faster.</p>
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Answers.CreationDate: 2024-07-14T20:54:10.290Z
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Body: <p>I know that Deep Learning is subset of Machine learning</p>
  <p>But is it correct that classical ML algorithms mainly focus on implementing Discriminative AI while DL algorithms implement both Generative AI and Discriminative AI?</p>
  
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Comments.CreationDate: 2024-07-15T19:54:11.977Z
Comments.Text: Today you subtly changed your question body from 'only' to 'mainly' about classical ML's focus on Discriminative AI. As you correctly listed before most known methods in any classical ML textbook are based on discriminative models such as svm, knn, logistic/linear/Poisson regressions, etc, thus apparently classical ML models mainly focus on discriminative models, compared to the very few classical generative models such as naive Bayes, GMMs, HMMs as I already mentioned in my yesterday's answer.
Comments.UserDisplayName: cinch
Tags: <machine-learning><deep-learning><comparison><generative-model><discriminative-model>
Title: Does Machine Learning focus on discriminative AI while Deep Learning also focus on generative AI?
UserDisplayName: DSP_CS
ThreadText: # Does Machine Learning focus on discriminative AI while Deep Learning also focus on generative AI?
  
  (Asked by: username_0 on 2024-07-14)
  
  I know that Deep Learning is subset of Machine learning
  
  But is it correct that classical ML algorithms mainly focus on implementing Discriminative AI while DL algorithms implement both Generative AI and Discriminative AI?
  
  **Tags:** machine-learning, deep-learning, comparison, generative-model, discriminative-model
  
  > **Comments:**
  > **username_1**: Today you subtly changed your question body from 'only' to 'mainly' about classical ML's focus on Discriminative AI. As you correctly listed before most known methods in any classical ML textbook are based on discriminative models such as svm, knn, logistic/linear/Poisson regressions, etc, thus apparently classical ML models mainly focus on discriminative models, compared to the very few classical generative models such as naive Bayes, GMMs, HMMs as I already mentioned in my yesterday's answer.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-07-14. Score: 1 | Accepted answer
  
  It's not accurate to say that classical machine learning implements only discriminative AI while deep learning implements both generative and discriminative AI. Both classical machine learning and deep learning have methods for both generative and discriminative tasks. For example, the classical [naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Relation_to_logistic_regression) for classification and some NLP tasks and GMMs used for clustering and density estimation are typically considered as a generative model.
  
  > In the case of discrete inputs (indicator or frequency features for discrete events), naive Bayes classifiers form a generative-discriminative pair with multinomial logistic regression classifiers: each naive Bayes classifier can be considered a way of fitting a probability model that optimizes the joint likelihood ${p(C,\mathbf {x} )}$, while logistic regression fits the same probability model to optimize the conditional $p(C\mid \mathbf {x} )$.[17]... Discriminative classifiers have lower asymptotic error than generative ones; however, research by Ng and Jordan has shown that in some practical cases naive Bayes can outperform logistic regression because it reaches its asymptotic error faster.
  
  
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