Can naive Bayes take categorical variables?

Can naive Bayes take categorical variables?

The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution. Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).

Can naive Bayes use continuous variables?

Typically, we use the Gaussian Naive Bayes model for variables on a continuous scale – assuming that our variables are normal distributed. In the equation above, we have 2 parameters we need to estimate, the mean μ of the samples associated with class ωj and the variance σ2 associated with class ωj, respectively.

Can a naive Bayes be used for categorical data?

I haven’t used scikit much before but I suppose that that Gaussian Naive Bayes is suitable for continuous data and that Bernoulli Naive Bayes can be used for categorical data. However, since I want to have both categorical and continuous data in my model, I don’t really know how to handle this. Any ideas would be much appreciated!

What’s the intuition behind continuous naive Bayes?

So here Naive Bayes is generating a Gaussian (Normal) distributions for each predictor variable. The distribution is characterized by two parameters, its mean and standard deviation. Then based on mean and standard deviation of the each predictor variable, the probability for a value to be ‘x’ is calculated using probability density function.

How is naive Bayes classification used in machine learning?

There are many ways to perform naive Bayes classification (NBC). A common technique in NBC is to recode the feature (variable) values into quartiles, such that values less than the 25th percentile are assigned a 1, 25th to 50th a 2, 50th to 75th a 3 and greater than the 75th percentile a 4.

Which is an example of Gaussian naive Bayes?

For example, Gaussian Naive Bayes assumes each category C has a different mean and variance: density p ( x ∣ C = i) = ϕ ( μ i, σ i 2). There are different ways to estimate the parameters, but typically one might: