2. Consider the following training set, which contains 3 binary attributes X, X, andX3. There are...

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2. Consider the following training set, which contains 3 binary attributes X₁, X₂, andX3. There are 50 examples in the training set, with equal number of positive and negative examples. X₁ 1 1 0 0 0 X2 X3 1 0 0 1 0 1 1 1 1 0 Number of positive training examples 5 10 5 0 5 Number of negative training examples 0 10 5 10 0 (a) Compute the class conditional probabilities P(X₁ = 1]+), P(X₁ = 1|−), P(X₂ = 1|+), P(X₂ = 1|-), P(X3 = 1|+), and P(X3 = 1|-) (b) Use the class conditional probabilities given in the previous question 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 rate of the naïve Bayes classifier. 2. Consider the following training set, which contains 3 binary attributes X₁, X₂, andX3. There are 50 examples in the training set, with equal number of positive and negative examples. X₁ 1 1 0 0 0 X2 X3 1 0 0 1 0 1 1 1 1 0 Number of positive training examples 5 10 5 0 5 Number of negative training examples 0 10 5 10 0 (a) Compute the class conditional probabilities P(X₁ = 1]+), P(X₁ = 1|−), P(X₂ = 1|+), P(X₂ = 1|-), P(X3 = 1|+), and P(X3 = 1|-) (b) Use the class conditional probabilities given in the previous question 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 rate of the naïve Bayes classifier.

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To compute the class conditional probabilities well start with part a Given there are an equal number of positive and negative examples and the total ... View the full answer