What is Gaussian function describe the Gaussian discriminant analysis?

What is Gaussian function describe the Gaussian discriminant analysis?

Gaussian Discriminant Analysis is a Generative Learning Algorithm and in order to capture the distribution of each class, it tries to fit a Gaussian Distribution to every class of the data separately. Below images depict the difference between the Discriminative and Generative Learning Algorithms.

Is Gaussian discriminant analysis generative or discriminative?

Gaussian Discriminant Analysis an example of Generative Learning Algorithms | by Parag Radke | Towards Data Science.

Is GDA generative or discriminative?

Gaussian discriminant analysis (GDA) model is a generative model where p(x|y) is a multi-variate Gaussian.

What is GDA in machine learning?

GDA, is a method for data classification commonly used when data can be approximated with a Normal distribution. As first step, you will need a training set, i.e. a bunch of data yet classified.

What is generative algorithm?

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.

How are generative learning algorithms used in Gaussian discriminant analysis?

Such algorithms try to model P (y|X) i.e. given a feature set X for a data sample what is the probability it belongs to the class ‘y’. On the other hand, Generative Learning Algorithms follow a different approach, they try to capture the distribution of each class separately instead of finding a decision boundary among classes.

Which is better Gaussian discriminant analysis or logistic regression?

Therefore, Gaussian Discriminant Analysis works quite well for a small amount of data (say a few thousand examples) and can be more robust compared to Logistic Regression if our underlying assumptions about the distribution of the data are true Reference: http://cs229.stanford.edu/notes2020spring/cs229-notes2.pdf

Which is the best algorithm for discriminant analysis?

Generative Learning Algorithms Discriminative Learning Algorithms include Logistic Regression, Perceptron Algorithm, etc. which try to find a decision boundary between different classes during the learning process.

Why are the contours of a Gaussian discriminant analysis noisy?

The surface of the models are not smooth (As the assumption p (x|y) is guassian is not perfectly true for this particular test data. And will discuss how it affects the accuracy of the model) and so the contours will also not be smooth but noisy. You can see the algorithm favours the class 0 for x0 and class 1 for x1 as expected.