I've seen the other thread here but I don't think the answer satisfied the actual question. What I have continually read is that Naive Bayes is a linear classifier (ex: here) (such that it draws a linear decision boundary) using the log odds demonstration.

However, I simulated two Gaussian clouds and fitted a decision boundary and got the results as such (library e1071 in r, using naiveBayes()) 1- Green, 0 - Red

As we can see, the decision boundary is non-linear. Is it trying to say that the parameters (conditional probabilities) are a linear combination in the log space rather than saying the classifier itself separates data linearly?

  • $\begingroup$ how did you create the decision boundary? i suspect its to do with your fitting routine rather than the true decision boundary of the classifier. normally one would generate a decision boundary by calculating the decision at every single point in your quadrant. $\endgroup$ – seanv507 Mar 18 '15 at 0:00
  • $\begingroup$ That is what i did, I took the two ranges of X = [Min(x), Max(x)] and Y = [Min(Y), Max(Y)] with a spacing of 0.1. I then fitted all those data points with the trained classifier and found points such that the log odds were between -0.05 and 0.05 $\endgroup$ – Kevin Pei Mar 18 '15 at 0:15

In general the naive Bayes classifier is not linear, but if the likelihood factors $p(x_i \mid c)$ are from exponential families, the naive Bayes classifier corresponds to a linear classifier in a particular feature space. Here is how to see this.

You can write any naive Bayes classifier as*

$$p(c = 1 \mid \mathbf{x}) = \sigma\left( \sum_i \log \frac{p(x_i \mid c = 1)}{p(x_i \mid c = 0)} + \log \frac{p(c = 1)}{p(c = 0)} \right),$$

where $\sigma$ is the logistic function. If $p(x_i \mid c)$ is from an exponential family, we can write it as

$$p(x_i \mid c) = h_i(x_i)\exp\left(\mathbf{u}_{ic}^\top \phi_i(x_i) - A_i(\mathbf{u}_{ic})\right),$$

and hence

$$p(c = 1 \mid \mathbf{x}) = \sigma\left( \sum_i \mathbf{w}_i^\top \phi_i(x_i) + b \right),$$


\begin{align} \mathbf{w}_i &= \mathbf{u}_{i1} - \mathbf{u}_{i0}, \\ b &= \log \frac{p(c = 1)}{p(c = 0)} - \sum_i \left( A_i(\mathbf{u}_{i1}) - A_i(\mathbf{u}_{i0}) \right). \end{align}

Note that this is similar to logistic regression – a linear classifier – in the feature space defined by the $\phi_i$. For more than two classes, we analogously get multinomial logistic (or softmax) regression.

If $p(x_i \mid c)$ is Gaussian, then $\phi_i(x_i) = (x_i, x_i^2)$ and we should have \begin{align} w_{i1} &= \sigma_1^{-2}\mu_1 - \sigma_0^{-2}\mu_0, \\ w_{i2} &= 2\sigma_0^{-2} - 2\sigma_1^{-2}, \\ b_i &= \log \sigma_0 - \log \sigma_1, \end{align}

assuming $p(c = 1) = p(c = 0) = \frac{1}{2}$.

*Here is how to derive this result:

\begin{align} p(c = 1 \mid \mathbf{x}) &= \frac{p(\mathbf{x} \mid c = 1) p(c = 1)}{p(\mathbf{x} \mid c = 1) p(c = 1) + p(\mathbf{x} \mid c = 0) p(c = 0)} \\ &= \frac{1}{1 + \frac{p(\mathbf{x} \mid c = 0) p(c = 0)}{p(\mathbf{x} \mid c = 1) p(c = 1)}} \\ &= \frac{1}{1 + \exp\left( -\log\frac{p(\mathbf{x} \mid c = 1) p(c = 1)}{p(\mathbf{x} \mid c = 0) p(c = 0)} \right)} \\ &= \sigma\left( \sum_i \log \frac{p(x_i \mid c = 1)}{p(x_i \mid c = 0)} + \log \frac{p(c = 1)}{p(c = 0)} \right) \end{align}

  • $\begingroup$ Thank you for the derivation, which I now understand, can you explain the notations in equation 2 and below? (u, h(x_i), phi(x_i), etc) Is P(x_i | c) under an exponential family just simply taking the value from the pdf? $\endgroup$ – Kevin Pei Mar 18 '15 at 15:50
  • $\begingroup$ There are different ways you can express one and the same distribution. The second equation is an exponential family distribution in canonical form. Many distributions are exponential families (Gaussian, Laplace, Dirichlet, Bernoulli, binomial, just to name a few), but their density/mass function is typically not given in canonical form. So you first have to reparametrize the distribution. This table tells you how to compute $\mathbf{u}$ (natural parameters) and $\phi$ (sufficient statistics) for various distributions: en.wikipedia.org/wiki/Exponential_family#Table_of_distributions $\endgroup$ – Lucas Mar 18 '15 at 16:28
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    $\begingroup$ Notice the important point that $\phi(x) = (x, x^2)$. What this means is that linear classifiers are a linear combination of weights $\mathbf{w}$ and potentially non-linear functions of the features! So, to the original poster's point, a plot of the datapoints may not show that they are separable by a line. $\endgroup$ – RMurphy Jun 20 '17 at 21:02
  • $\begingroup$ I find this answer misleading: as pointed out in the comment just about, and the answer just below, the Gaussian naive Bayes is not linear in the original feature space, but in a non-linear transform of these. Hence it is not a conventional linear classifier. $\endgroup$ – Gael Varoquaux Oct 1 '18 at 16:58
  • $\begingroup$ why $p(x_i|c)$ is Gaussian,then $\phi_i(x_i)=(x_i,x_i^2)$? I think the sufficient statistic $T(x)$ for Gaussian distribution should be $x/\sigma$. $\endgroup$ – Naomi Nov 26 '18 at 5:59

It is linear only if the class conditional variance matrices are the same for both classes. To see this write down the ration of the log posteriors and you'll only get a linear function out of it if the corresponding variances are the same. Otherwise it is quadratic.


I'd like add one additional point: the reason for some of the confusion rests on what it means to be performing "Naive Bayes classification".

Under the broad topic of "Gaussian Discriminant Analysis (GDA)" there are several techniques: QDA, LDA, GNB, and DLDA (quadratic DA, linear DA, gaussian naive bayes, diagonal LDA). [UPDATED] LDA and DLDA should be linear in the space of the given predictors. (See, e.g., Murphy, 4.2, pg. 101 for DA and pg. 82 for NB. Note: GNB is not necessarily linear. Discrete NB (which uses a multinomial distribution under the hood) is linear. You can also check out Duda, Hart & Stork section 2.6). QDA is quadratic as other answers have pointed out (and which I think is what is happening in your graphic - see below).

These techniques form a lattice with a nice set of constraints on the "class-wise covariance matrices" $\Sigma_c$:

  • QDA: $\Sigma_c$ arbitrary: arbitrary ftr. cov. matrix per class
  • LDA: $\Sigma_c = \Sigma$: shared cov. matrix (over classes)
  • GNB: $\Sigma_c = {diag}_c$: class wise diagonal cov. matrices (the assumption of ind. in the model $\rightarrow$ diagonal cov. matrix)
  • DLDA: $\Sigma_c = diag$: shared & diagonal cov. matrix

While the docs for e1071 claim that it is assuming class-conditional independence (i.e., GNB), I'm suspicious that it is actually doing QDA. Some people conflate "naive Bayes" (making independence assumptions) with "simple Bayesian classification rule". All of the GDA methods are derived from the later; but only GNB and DLDA use the former.

A big warning, I haven't read the e1071 source code to confirm what it is doing.


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