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Machine Learning For Dummies
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Even if you have enough examples at hand for training both simple and complex learning algorithms, they must present complete values in the features, without any missing data. Having an incomplete example makes connecting all the signals within and between features impossible. Missing values also make it difficult for the algorithm to learn during training. You must do something about the missing data. Most often, you can ignore missing values or repair them by guessing a likely replacement value. However, too many missing values render more uncertain predictions because missing information could conceal any possible figure; consequently, the more missing values in the features, the more variable and imprecise the predictions.
As a first step, count the number of missing cases in each variable. When a variable has too many missing cases, you may need to drop it from the training and test dataset. A good rule of thumb is to drop a variable...
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