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Laplace smoothing is a way to move probabilities towards uninformed mean. Suppose you have a multinomial variable with sample counts $c_1, c_2,..,c_d$, where $d$ is the number of dimensions. A Laplace smoothed version of estimated probabilities has the form: $(c_i + \alpha)/(N + d\alpha)$, where $\alpha$ is positive. If $\alpha$ is $0$ then we have non ...


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Sorry this is certainly too late to help you, but answering in case others find the question. When using Laplace smoothing, you should apply the smoothing for all attributes. To see why this is important, consider a dataset containing exactly 1 instance of a certain attribute and 0 instances of another. If you only applied the smoothing factor to the ...


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