Consider a training set with 3 features, X1, X2, and X3, for a binary classification problem....

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Consider a training set with 3 features, X1, X2, and X3, for a binary classification problem. The class distribution is shown in the table below. X1 X2 X3 Number of positives Number of negatives 1 1 1 1 0 0 20 0 1 0 0 0 0 2255 20 8 17 8 17 a) Based on the information above, determine whether X and X2 are independent of each other. (Hint: If X and X2 are independent, then P(X1, X2) = P(X) P(X2) for ALL possible values of X and X2) - (3 points) b) Determine whether X1 and X2 are conditionally independent of each other given the class. (Hint: If X and X2 are conditionally independent, then P(X1, X2|+) = P(X|+) P(X2|+) and P(X1, X2|) = P(X1|+) P(X2|-) for all X and X2) Activate Windows Go to Settings to activate W Consider a training set with 3 features, X1, X2, and X3, for a binary classification problem. The class distribution is shown in the table below. X1 X2 X3 Number of positives Number of negatives 1 1 1 1 0 0 20 0 1 0 0 0 0 2255 20 8 17 8 17 a) Based on the information above, determine whether X and X2 are independent of each other. (Hint: If X and X2 are independent, then P(X1, X2) = P(X) P(X2) for ALL possible values of X and X2) - (3 points) b) Determine whether X1 and X2 are conditionally independent of each other given the class. (Hint: If X and X2 are conditionally independent, then P(X1, X2|+) = P(X|+) P(X2|+) and P(X1, X2|) = P(X1|+) P(X2|-) for all X and X2) Activate Windows Go to Settings to activate W

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a To determine if X1 and X2 are independent of each other we need to chec... View the full answer