Question

Suppose you have a binary classification problem (class y0 vs class y1) with 2 binary features (x0 and x1). You intend to use a Naive Bayes classifier. Your dataset consists of 6 data points (equally distributed between y0 and y1), three of which have the same feature values, i.e. x0 = x1 = 1. However, two of these data points belong to y0, whereas the third data point belongs to y1. After training the Naive Bayes classifier on this dataset, what will its output be for the input: (x0, x1) = (1, 1)? Data points are not enough to train this Naive Bayes classifier because it has more parameters. Insufficient information. The answer depends on the remaining three data points. Equal probability 0.5 for both classes. Probability 2/6 for class y0 and 1/6 for class y1.

          Suppose you have a binary classification problem (class y0 vs class y1) with 2 binary features (x0 and x1). You intend to use a Naive Bayes classifier. Your dataset consists of 6 data points (equally distributed between y0 and y1), three of which have the same feature values, i.e. x0 = x1 = 1. However, two of these data points belong to y0, whereas the third data point belongs to y1. After training the Naive Bayes classifier on this dataset, what will its output be for the input: (x0, x1) = (1, 1)?

Data points are not enough to train this Naive Bayes classifier because it has more parameters.
Insufficient information. The answer depends on the remaining three data points.
Equal probability 0.5 for both classes.
Probability 2/6 for class y0 and 1/6 for class y1.

suppose you have a binary classification problem class y0 vs class y1 with 2 binary features xo and x1you intend to use a naive bayes classifier your dataset consists of 6 data points equall 95535

Added by Crystal C.

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