The naive Bayes (NB) technique is a machine learning approach for classification. There are four main types of NB that vary according to the type of data they work with. All four variations of NB can work with binary classification (e.g, predict the sex of a person) or with multi-class classification (e.g, predict the State a person lives in).
Briefly, the four types of NB are:
1. Categorical: the predictors are all categorical, like “red” or “blue”.
2. Multinomial: the predictors are all integer counts.
3. Bernoulli: the predictors are all Boolean/binary.
4. Gaussian: the predictors are all numeric.
Here are some examples.
1. Categorical NB Predict a person's job type from State, sex, race alaska male white technical georgia male white management oregon female asian sales . . .
2. Multinomial NB Predict a college course type from counts of each A-F grade 5 9 16 3 1 mathematics 4 7 11 0 0 psychology 6 6 9 2 1 history . . .
3. Bernoulli NB Predict a person's political party from votes on six motions yes no yes yes no no democrat yes yes yes no yes yes republican no no yes no no no republican . . .
Note: Notice that Bernoulli NB is really just a special case of Categorical NB.
4. Gaussian NB Predict a student's happiness from age, height, GPA 0.21 0.72 0.300 high 0.19 0.65 0.325 low 0.20 0.70 0.297 medium . . .
I remember one of the graduate classes I took at USC from Dr. Dennis Hocevar, one of my mentors. It was a statistics class that emphasized the importance of identifying what type of problem you were facing.
The “naive” in naive Bayes classification means unsophisticated because each predictor variable is analyzed independently and interactions between predictors aren’t used. I like movies where young naive characters discover their power. Left: Luke in “Star Wars” (1977). Center: Matilda in “Matilda” (1996). Right: Harry in “Harry Potter and the Sorcerer’s Stone” (2001).
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