Say we have used the TFIDF transform to encode documents into continuous-valued features.

How would we now use this as input to a Naive Bayes classifier?

Bernoulli naive-bayes is out, because our features aren't binary anymore.
Seems like we can't use Multinomial naive-bayes either, because the values are continuous rather than categorical.

As an alternative, would it be appropriate to use gaussian naive bayes instead? Are TFIDF vectors likely to hold up well under the gaussian-distribution assumption?

The sci-kit learn documentation for MultionomialNB suggests the following:

The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.

Isn't it fundamentally impossible to use fractional values for MultinomialNB?
As I understand it, the likelihood function itself assumes that we are dealing with discrete-counts:

(From Wikipedia):

${\displaystyle p(\mathbf {x} \mid C_{k})={\frac {(\sum _{i}x_{i})!}{\prod _{i}x_{i}!}}\prod _{i}{p_{ki}}^{x_{i}}}$

How would TFIDF values even work with this formula, since the $x_i$ values are all required to be discrete counts?

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    $\begingroup$ Hi dhrumeel, was your question answered? If so, could you please accept the correct answer with the checkmark beside it? Otherwise, what can be clarified? $\endgroup$ Apr 3, 2021 at 15:57

2 Answers 2


This is possible without discretizing your counts or changing the form of your model to something with less natural assumptions (e.g. Gaussian).

The likelihood for a multinomial distribution can be expressed the way you've written it, but it can also be written differently to allow for nonnegative real counts.

$$p(\mathbf {x} \mid C_{k}) =\frac { \left( \sum _{i}x_{i} \right)! }{ \prod_{i}x_{i}! } \prod_{i}{p_{ki}}^{x_{i}} = \frac{ \Gamma\left(1 + \sum _{i}x_{i}\right) }{ \prod_{i} \Gamma\left(1 + x_{i}\right) } \prod_{i}{p_{ki}}^{x_{i}} $$

This comes from the identity $n! = \Gamma(n+1)$. The gamma function generalizes the factorial function to nonnegative reals. (It generalizes also to negative non-integral reals, but that's not relevant.)

In this latter form, you can use non-integral counts, like tf-idf scores for words or pseudocounts from a fractional Dirichlet prior.

scikit-learn handles non-integral counts just fine, by the way.

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    $\begingroup$ Quick follow up question - while the Multinomial can be used as a likelihood for TF-IDF observations, what would you recommend using? Alternatively, what likelihood is commonly used? I asked this question stats.stackexchange.com/questions/520216/… , if you want credit for answering it $\endgroup$ Apr 17, 2021 at 18:47
  • $\begingroup$ Thanks for the answer. I am trying to understand the connection between the distribution assumption and the use of Gamma function in the posterior distribution. For example if my data follow a Gamma distribution. i.e., we have non-negative values, how can we justify the use of Multinomial Naive-Bayes with respect to the formula you gave? Any help is highly appreciated. $\endgroup$
    – Thoth
    Jun 20, 2023 at 13:47
  • $\begingroup$ @Arya McCarthy I did a relevant post. Any advise is highly appreciated. $\endgroup$
    – Thoth
    Jun 22, 2023 at 17:10

Maybe you can try to discretize those continuous values, and this question is equivalent to this one: How does Naive Bayes work with continuous variables?. Or you can try Gaussian Naive Bayes: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html


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