Why features are independent in naive Bayes?
Naive Bayes is so called because the independence assumptions we have just made are indeed very naive for a model of natural language. The conditional independence assumption states that features are independent of each other given the class. This is hardly ever true for terms in documents.
Is naive Bayes sensitive to irrelevant features?
As a result of these assumptions, naive Bayes’ classifiers are not sensitive to irrelevant features (assumption of equal weights) and are fast to train & predict (assumption of event independence).
What is feature independence?
Basically that means that combinations of features are treated in just the same way as each feature appearing on its own.
Why is the Bayes model called Naive Bayes?
Naive Bayes is so called because the independence assumptions we have just made are indeed very naive for a model of natural language. The conditional independence assumption states that features are independent of each other given the class. This is hardly ever true for terms in documents.
Can you use naive Bayes to classify handwritten numbers?
Indeed, we will get to implement Naive Bayes to classify images of handwritten numbers later. Let’s now dig dipper into the intuitions, and assume that we have two variables A and B that are independent of each other. By applying the product probability we know that:
How is the prior determined in a Bayes model?
The nice thing about this Bayesian framework is that we can easily determine the likelihood by modelling it after a probability distribution, and the prior can be found by just counting how many appearances for yk we have in our dataset and then dividing by the total number of data points.
How is Bayes theorem used in generative models?
Indeed, by knowing the likelihood value as well as the prior, we are able to make predictions based on our data. In conclusion, a generative model will use Bayes theorem to make predictions by using the likelihood and the prior.