[algorithm] What is the difference between a generative and a discriminative algorithm?

What is the difference between a generative and a discriminative algorithm?

An addition informative point that goes well with the answer by StompChicken above.

The fundamental difference between discriminative models and generative models is:

Discriminative models learn the (hard or soft) boundary between classes

Generative models model the distribution of individual classes

Edit:

A Generative model is the one that can generate data. It models both the features and the class (i.e. the complete data).

If we model P(x,y): I can use this probability distribution to generate data points - and hence all algorithms modeling P(x,y) are generative.

Eg. of generative models

• Naive Bayes models P(c) and P(d|c) - where c is the class and d is the feature vector.

  Also, P(c,d) = P(c) * P(d|c)

  Hence, Naive Bayes in some form models, P(c,d)

• Bayes Net

• Markov Nets

A discriminative model is the one that can only be used to discriminate/classify the data points. You only require to model P(y|x) in such cases, (i.e. probability of class given the feature vector).

Eg. of discriminative models:

• logistic regression

• Neural Networks

• Conditional random fields

In general, generative models need to model much more than the discriminative models and hence are sometimes not as effective. As a matter of fact, most (not sure if all) unsupervised learning algorithms like clustering etc can be called generative, since they model P(d) (and there are no classes:P)

PS: Part of the answer is taken from source

A generative algorithm model will learn completely from the training data and will predict the response.

A discriminative algorithm job is just to classify or differentiate between the 2 outcomes.

The short answer

Many of the answers here rely on the widely-used mathematical definition [1]:

• Discriminative models directly learn the conditional predictive distribution p(y|x).
• Generative models learn the joint distribution p(x,y) (or rather, p(x|y) and p(y)).
  • Predictive distribution p(y|x) can be obtained with Bayes' rule.

Although very useful, this narrow definition assumes the supervised setting, and is less handy when examining unsupervised or semi-supervised methods. It also doesn't apply to many contemporary approaches for deep generative modeling. For example, now we have implicit generative models, e.g. Generative Adversarial Networks (GANs), which are sampling-based and don't even explicitly model the probability density p(x) (instead learning a divergence measure via the discriminator network). But we call them "generative models” since they are used to generate (high-dimensional [10]) samples.

A broader and more fundamental definition [2] seems equally fitting for this general question:

• Discriminative models learn the boundary between classes.
  • So they can discriminate between different kinds of data instances.
• Generative models learn the distribution of data.
  • So they can generate new data instances.

Image source

A closer look

Even so, this question implies somewhat of a false dichotomy [3]. The generative-discriminative "dichotomy" is in fact a spectrum which you can even smoothly interpolate between [4].

As a consequence, this distinction gets arbitrary and confusing, especially when many popular models do not neatly fall into one or the other [5,6], or are in fact hybrid models (combinations of classically "discriminative" and "generative" models).

Nevertheless it's still a highly useful and common distinction to make. We can list some clear-cut examples of generative and discriminative models, both canonical and recent:

• Generative: Naive Bayes, latent Dirichlet allocation (LDA), Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), normalizing flows.
• Discriminative: Support vector machine (SVM), logistic regression, most deep neural networks.

There is also a lot of interesting work deeply examining the generative-discriminative divide [7] and spectrum [4,8], and even transforming discriminative models into generative models [9].

In the end, definitions are constantly evolving, especially in this rapidly growing field :) It's best to take them with a pinch of salt, and maybe even redefine them for yourself and others.

Sources

1. Possibly originating from "Machine Learning - Discriminative and Generative" (Tony Jebara, 2004).
2. Crash Course in Machine Learning by Google
3. The Generative-Discriminative Fallacy
4. "Principled Hybrids of Generative and Discriminative Models" (Lasserre et al., 2006)
5. @shimao's question
6. Binu Jasim's answer
7. Comparing logistic regression and naive Bayes:
   • cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf
   • "On Discriminative vs. Generative classifiers"
   • Comment on "On Discriminative vs. Generative classifiers"
8. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/DengJaitly2015-ch1-2.pdf
9. "Your classifier is secretly an energy-based model" (Grathwohl et al., 2019)
10. Stanford CS236 notes: Technically, a probabilistic discriminative model is also a generative model of the labels conditioned on the data. However, the term generative models is typically reserved for high dimensional data.

A generative algorithm models how the data was generated in order to categorize a signal. It asks the question: based on my generation assumptions, which category is most likely to generate this signal?

A discriminative algorithm does not care about how the data was generated, it simply categorizes a given signal.

My two cents: Discriminative approaches highlight differences Generative approaches do not focus on differences; they try to build a model that is representative of the class. There is an overlap between the two. Ideally both approaches should be used: one will be useful to find similarities and the other will be useful to find dis-similarities.

In practice, the models are used as follows.

In discriminative models, to predict the label y from the training example x, you must evaluate:

which merely chooses what is the most likely class y considering x. It's like we were trying to model the decision boundary between the classes. This behavior is very clear in neural networks, where the computed weights can be seen as a complexly shaped curve isolating the elements of a class in the space.

Now, using Bayes' rule, let's replace the in the equation by . Since you are just interested in the arg max, you can wipe out the denominator, that will be the same for every y. So, you are left with

which is the equation you use in generative models.

While in the first case you had the conditional probability distribution p(y|x), which modeled the boundary between classes, in the second you had the joint probability distribution p(x, y), since p(x | y) p(y) = p(x, y), which explicitly models the actual distribution of each class.

With the joint probability distribution function, given a y, you can calculate ("generate") its respective x. For this reason, they are called "generative" models.

Here's the most important part from the lecture notes of CS299 (by Andrew Ng) related to the topic, which really helps me understand the difference between discriminative and generative learning algorithms.

Suppose we have two classes of animals, elephant (y = 1) and dog (y = 0). And x is the feature vector of the animals.

Given a training set, an algorithm like logistic regression or the perceptron algorithm (basically) tries to find a straight line — that is, a decision boundary — that separates the elephants and dogs. Then, to classify a new animal as either an elephant or a dog, it checks on which side of the decision boundary it falls, and makes its prediction accordingly. We call these discriminative learning algorithm.

Here's a different approach. First, looking at elephants, we can build a model of what elephants look like. Then, looking at dogs, we can build a separate model of what dogs look like. Finally, to classify a new animal, we can match the new animal against the elephant model, and match it against the dog model, to see whether the new animal looks more like the elephants or more like the dogs we had seen in the training set. We call these generative learning algorithm.

This article helped me a lot in understanding the concept.

In summary,

• Both are probabilistic models, meaning they both use probability (conditional probability , to be precise) to calculate classes for the unknown data.
• The Generative Classifiers apply Joint PDF & Bayes Theorem on the data set and calculate conditional probability using values from those.
• The Discriminative Classifiers directly find Conditional probablity on the data set

Some good reading material: conditional probability , Joint PDF

All previous answers are great, and I'd like to plug in one more point.

From generative algorithm models, we can derive any distribution; while we can only obtain the conditional distribution P(Y|X) from the discriminative algorithm models(or we can say they are only useful for discriminating Y’s label), and that's why it is called discriminative model. The discriminative model doesn't assume that the X's are independent given the Y($X_i \perp X_{-i} | Y$) and hence is usually more powerful for calculating that conditional distribution.

The different models are summed up in the table below:

Image source: Supervised Learning cheatsheet - Stanford CS 229 (Machine Learning)

Imagine your task is to classify a speech to a language.

You can do it by either:

1. learning each language, and then classifying it using the knowledge you just gained

or

2. determining the difference in the linguistic models without learning the languages, and then classifying the speech.

The first one is the generative approach and the second one is the discriminative approach.

Check this reference for more details: http://www.cedar.buffalo.edu/~srihari/CSE574/Discriminative-Generative.pdf.

Generally, there is a practice in machine learning community not to learn something that you don’t want to. For example, consider a classification problem where one's goal is to assign y labels to a given x input. If we use generative model

p(x,y)=p(y|x).p(x)

we have to model p(x) which is irrelevant for the task in hand. Practical limitations like data sparseness will force us to model p(x) with some weak independence assumptions. Therefore, we intuitively use discriminative models for classification.