Generative Learning algorithms implementation using numpy. I implemented Gaussian discriminant analysis and Naive Bayes.
The generative model is the model that first try to learn what each object might look like. Then, based on input, it gives a probability of the input being this class.
In this model, we’ll assume that p(x|y) is distributed according to a multivariate normal distribution. Let’s talk briefly about the properties of multivariate normal distributions before moving on to the GDA model itself.
Using Bayes theorem, we can find the probability of A happening, given that B has occurred. Here, B is the evidence and A is the hypothesis. The assumption made here is that the predictors/features are independent. That is presence of one particular feature does not affect the other. Hence it is called naive.source blog
pip install -r requirements.txt
refer to usage notebook