Perform Gaussian Discriminant Analysis (GDA) on the spambase data with k-fold cross validation.
For each fold, use 1 fold for testing and k-1 folds for training.
During the analysis, you should test both of the following covariance assumptions:
In the context of GDA, the former is called Linear Discriminant Analysis while the latter is called Quadratic Discriminant Analysis.
Based on the training and testing performance, does it appear the data are normally distributed?
Create a set of Naive Bayes classifiers for detecting e-mail spam and test the classifiers on the Spambase dataset via 10-fold cross validation.
Create three distinct Naive Bayes classifiers by varying the likelihood distribution. In particular, try:
Modeling the likelihood using a Bernoulli distribution
Modeling the likelihood using a Gaussian distribution
Modeling the likelihood non-parameterically
For real-valued features, a Bernoulli likelihood may be obtained by thresholding against a scalar-valued statistic, such as the sample mean \(\mu\). Similarly, a Gaussian likelihood function can be obtained by estimating the [class conditional] expected value \(\mu\) and variance \(\sigma\). For the non-parameteric setting, model the feature distribution either with a kernel density estimate (KDE) or a histogram.
Bernoulli example: Consider some threshold \(\mu \in \mathbb{R}\). To compute the conditional probability of a feature \(f_i\), compute the fraction by which \(f_i\) is above or below \(\mu\) over all the data within its class. In other words, for feature \(f_i\) with expected value \(\mu_i\), estimate: \[ P(f_i \leq \mu_i \mid \text{spam} ) \text{ and } P(f_i > \mu_i \mid \text{spam} )\] \[ P(f_i \leq \mu_i \mid \text{non-spam} ) \text{ and } P(f_i > \mu_i \mid \text{non-spam} )\] and use these estimated values in your Naive Bayes predictor.
For all the models mentioned, you may want to consider using some kind of additive smoothing technique as a regularizer.
Evaluate the performance of the classifiers using the following three performance summaries.
Error tables: Create a table with one row per fold showing the false positive, false negative, and overall error rates of the classifiers. Add one final row per table yielding the average error rates across all folds.
ROC Curves: Graph the Receiver Operating Characteristic (ROC) curve for each of your classifiers on Fold 1 and calculate their area-under-the-curve (AUC) statistics. You should draw all three curves on the same plot so that you can compare them.
For this problem, the false positive rate is the fraction of non-spam testing examples that are misclassified as spam, while the false negative rate is the fraction of spam testing examples that are misclassified as non-spam.
Context: In many situations, false positive (Type I) and false negative (Type II) errors incur different costs. In spam filtering, for example, a false positive is a legitimate e-mail that is misclassified as spam (and perhaps automatically redirected to a “spam folder” or, worse, auto-deleted) while a false negative is a spam message that is misclassified as legitimate (and sent to one’s inbox).
When using Naive Bayes, one can easily make such trade-offs. For example, in the usual Bayes formulation, with \(\mathbf{x}\) the data vector and \(y\) the class variable, one would predict “spam” if:
\[ P(y = \text{spam} | \mathbf{x}) > P(y = \text{non-spam} | \mathbf{x}) \]
or equivalently, in a log-odds formulation,
\[ \ln(P(y = \text{spam} | \mathbf{x}) / P(y = \text{non-spam} | \mathbf{x})) > 0 \]
However, note that one could choose to classify an e-mail as spam for any threshold \(\tau\), i.e.:
\[ \ln(P(y = \text{spam} | \mathbf{x}) / P(y = \text{non-spam} | \mathbf{x})) > \tau \]
Larger values of \(\tau\) reduce the number of spam classifications, reducing false positives at the expense of more false negatives. Negative values of \(\tau\) have the converse effect.
Most users are willing to accept some false negative examples so long as very few legitimate e-mails are misclassified. Given your classifiers and their ROC curves, what value of \(\tau\) would you choose to deploy in a real email spam filter?
Use the pdf of the expectation maximization (EM) code demo’ed in class, annotate the source code with explanations of what the code is doing. Your annotations should be text comments aligned on the right margin of the source code.
Run the EM algorithm with random initial parameter values and see if you recover the true parameters of the underlying generative model.
\[ \mu_1 = [3,3], \quad \Sigma_1 = \begin{bmatrix} 1 & 0 \\ 0 & 3 \end{bmatrix}, \quad n_1=2000 \]
\[ \mu_1 = [7,4], \quad \Sigma_2 = \begin{bmatrix} 1 & 0.5 \\ 0.5 & 1 \end{bmatrix}, \quad n_1=4000 \]
\[ \mu_1 = [3,3], \quad \Sigma_1 = \begin{bmatrix} 1 & 0 \\ 0 & 3 \end{bmatrix}, \quad n_1=2000 \]
\[ \mu_2 = [7,4], \quad \Sigma_2 = \begin{bmatrix} 1 & 0.5 \\ 0.5 & 1 \end{bmatrix}, \quad n_2=3000 \]
\[ \mu_3 = [5,7], \quad \Sigma_3 = \begin{bmatrix} 1 & 0.2 \\ 0.2 & 1 \end{bmatrix}, \quad n_3=5000 \]
Verify your findings against the true parameters via your choice of summary (e.g. absolute parameters differences, contour plots, etc.)
Cheng’s note summarizing E an M steps for this problem.
A. Generate mixture data (coin flips). Pick a p,r,pi as in the EM example discussed in class (or in notes). Say p=.75, r=.4, pi=.8, but you should try this for several sets of values. Generate the outcome of the coin experiment by first picking a coin (pi probability for first coin, 1-pi probability for the second coin), then flip that coin K times (use K=10) with probability of head (p if first coin is picked, r if the second coin is picked) and finally write down a 1 if the head is seen, 0 for the tail. Repeat this M=1000 times or more; so your outcome is a stream of M sequences of K flips : (1001110001; 0001110001; 1010100101 etc)
B. Infer parameters from data. Now using the stream of 1 and 0 observed, recover p,r,pi using the EM algorithm; K is known in advance. Report in a table the parameter values found by comparison with the ones used to generate data; try several sets of (p,r,pi). Here are some useful notes, and other readings (1 , 2 , 3 , 4, 5, ) for the coin mixture.
C. Repeat A) and B) with T coins instead of two. You will need more mixture parameters.
Rerun the Naive Bayes classifier on Spam Data. For each feature (1-dim) use a mixture of K Gaussians as your model; run the EM to estimate the 3*K parameters for each feature : mean1, var1, w1; mean2,var2, w2;… meanK,varK,wK; constrained by w1+w2 +…+wK=1. (You would need a separate mixture for positive and negative data, for each feature). We observed best results for K=9.
Testing: for each testing point, apply the Naive Bayes classifier as before: take the log-odd of product of probabilities over features mixtures (and the prior), separately for positive side and negative side; use the overall probability ratio as an output score, and compute the AUC for testing set. Do this for a 10-fold cross validation setup. Is the overall 10-fold average AUC better than before, when each feature model was a single Gaussian?
Somebody tosses a fair coin and if the result is heads, you get nothing, otherwise you get $5. How much would you be pay to play this game? What if the win is $500 instead of $5?
Suppose you play instead the following game:
Would you be willing to play this game (why)?
d)(difficult) After about how many games (estimate) the probability of making a profit overall is bigger than 50% ?
DHS CH2, Pb 43
DHS Pb 3.50 (page 154-155) The standard method for calculating the probability of a sequence in a given HMM is to use the forward probabilities αi(t).