As we know the Bernoulli Naive Bayes Classifier uses binary predictors (features). The thing I am not getting is how BernoulliNB in scikit-learn is giving results even if the predictors are not binary. The following example is taken verbatim from the documentation:
import numpy as np
rng = np.random.RandomState(1)
X = rng.randint(5, size=(6, 100))
Y = np.array([1, 2, 3, 4, 4, 5])
from sklearn.naive_bayes import BernoulliNB
clf = BernoulliNB()
clf.fit(X, Y)
print(clf.predict(X[2:3]))
Output:
array([3])
Here are the first 10 features of X, and they are obviously not binary:
3 4 0 1 3 0 0 1 4 4 1
1 0 2 4 4 0 4 1 4 1 0
2 4 4 0 3 3 0 3 1 0 2
2 2 3 1 4 0 0 3 2 4 1
0 4 0 3 2 4 3 2 4 2 4
3 3 3 3 0 2 3 1 3 2 3
How does BernoulliNB work here even though the predictors are not binary?
This is due to the binarize argument; from the docs:
binarize : float or None, default=0.0
Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
When called with its default value binarize=0.0, as is the case in your code (since you do not specify it explicitly), it will result in converting every element of X greater than 0 to 1, hence the transformed X that will be used as the actual input to the BernoulliNB classifier will consist indeed of binary values.
The binarize argument works exactly the same way with the stand-alone preprocessing function of the same name; here is a simplified example, adapting your own:
from sklearn.preprocessing import binarize
import numpy as np
rng = np.random.RandomState(1)
X = rng.randint(5, size=(6, 1))
X
# result
array([[3],
[4],
[0],
[1],
[3],
[0]])
binarize(X) # here as well, default threshold=0.0
# result (binary values):
array([[1],
[1],
[0],
[1],
[1],
[0]])