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A na�ve Bayes classifier takes in a training set of these bags of words that are already classified.
Based on past data in the training set, perhaps one or both classes have never seen this word.
Surveying customers to build a training set is always an option.
In linear regression, outliers (values that are markedly distant from the rest of the data) in your training set have more pull and can throw off the model fitting process.
If you knew nothing about a customer in the training set (columns B through T were missing) but you were forced to make a prediction on pregnancy anyway, the best way to minimize the sum of squared error in that case would be to just
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