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# Hi. How are you? I have these training data points: Label 0: (-2 , 1) , (-1, 1) , (0, 1) , (1,

1) , (2, 1.10)

Label 1: (1.10 , -2) , (1 , -1) , (1 , 0) , (1, 1) , (1, 2)

And I have these two test data points:

Label 0: (-3 , 1)

Label 1: (1 , 3)

I have to classify the test points using a Naive Bayes Classifier trained on the training points.

The mean for Label 0 training points I calculate to be (0 , 1.02)

The mean for Label 1 training points I calculate to be (1.02 , 0)

The variance for Label 0 training points I calculate to be (2 , 0.0016)

The variance for Label 1 training points I calculate to be (0.0016 , 2)

The Naive Bayes classifier is ( Data - Mean)^2 / Variance for each column/dimension, and then summed. Since we have two columns/dimensions, we will do two calculations for each testing point, sum them, and see which one has the higher value.

Therefore, for Label 0, I get (((-3 - 0)^2)/2) + (((1 - 1.02)^2)/0.0016) and for Label 0, I get (((1 - 1.02)^2)/0.0016) + (((3 - 0)^2)/2).

They both equal 4.75. So, how can I classify them?

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