Consider a training set with 3 features, X1, X2and X3, for a binary classification problem. The distribution of the data set is shown in the table below.

Based on the information above, determine whether X1 and X2 are conditionally independent of each other given the class.

Compute the class conditional probabilities P(X1=1|+), P(X1=1|-), P(X2 = 1|+), P(X2 = 1|-), P(X3 = 1|+), and P(X3 = 1|-).

Use those class conditional probabilities to predict the class label of each example with the feature set given in the training set above. Use your results to compute the training error of the naive Bayes classifier.

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