Description
A Na¨ıve Bayes Classifier is a specific way of constructing plugin classifiers which is quite useful
in practice. We assume that data are described in terms of d-dimensional feature vectors x =
(x1, x2, . . . , xd).
Thus, for a labeled example (X, Y ) ∈ X × Y drawn from the unknown data
distribution P, the random variable X can be described as a random vector X = (X1, X2, . . . , Xd).
In the Na¨ıve Bayes Classifier, the na¨ıve assumption made is that the d features are independent
of each other conditioned on the class; i.e. for any label y ∈ Y, and any values of the d features
(x1, x2, . . . , xd), we have
Pr[(X1, X2, . . . , Xd) = (x1, x2, . . . , xd) | Y = y] = Y
d
i=1
Pr[Xi = xi
| Y = y].
A Na¨ıve Bayes Classifier is the plugin classifier obtained when a specific parametric model is assumed for each class conditional feature distribution and performing maximum likelihood estimation
for each feature separately.
Consider a setting where we have binary features, i.e. all xi are in {0, 1}. We will assume that
the class conditional distribution of each xi
is Bernoulli.
Part 1 of assignment: Briefly describe the procedure and formulas for computing a Na¨ıve
Bayes Classifier for binary features with Bernoulli class conditional distributions.
Next, download the data file usps.mat from Canvas and load it into MATLAB. This will generate
two matrices: train usps, the training set, with 10000 labeled examples, and test usps, the test
set, with 1000 labeled examples. The examples represent 16 × 16 images of the handwritten digits
from 0 to 9. Thus each example has 256 features, corresponding to the 256 pixels, and each feature
is binary corresponding to whether the pixel is black or white. Each matrix has 257 columns, and
each row corresponds to a labeled example. In each row, the first 256 coordinates are the binary
features for the image, and the 257-th feature is the label, which is one of {0, 1, 2, . . . , 9}. If you
want to view the i-th image in the training data, use
imagesc(reshape(train usps(i, 1:256), 16, 16));
The colors may be a little jarring; try the following to fix that:
colormap(1-gray);
Write a MATLAB function
function params = hw1 train NB(train data)
which computes the parameters for a Na¨ıve Bayesian Classifier using maximum likelihood estimation with a Bernoulli model class for each feature. The output params can be any MATLAB data
structure that works, no specific format is desired. Use this function to compute the parameters
for the classifier with train data = train usps.
Write another function which computes predictions for test data given the parameters computed
by the hw1 train NB function, and computes the total number of errors made on the test data,
with the following signature:
function loss = hw1 test NB(params, test data)
Part 2 of assignment: Run this function with params computed previously and test data
= train usps to compute the training error, and with test data = test usps to compute test
error, and report both.
Write another function which computes predictions for test data using a k-nearest neighbor
classifier with the `2 norm using training data, and outputs the total number of errors made on the
test data, as follows:
function loss = hw1 kNN(k, train data, test data)
In order to speed computation, you may find it useful to vectorize your code as much as possible:
see https://www.mathworks.com/help/matlab/matlab_prog/vectorization.html.
Part 3 of assignment: Run hw1 NN with k = 1, 3, 5, train data = train usps and test data
= train data(1:1000, 🙂 to compute the training error on the first 1000 training examples, and
report it for each value of k. Repeat the computation with test data = test usps to compute
test error, and report it for each value of k.
