With Bayes classifier, missing data items are

treated as equal compares.
treated as unequal compares.
replaced with a default value.
ignored.

The correct answer is: D. ignored.

Bayes classifiers are a type of machine learning algorithm that can be used to classify data. They work by calculating the probability that a given data point belongs to each class, and then assigning the data point to the class with the highest probability.

When a Bayes classifier is trained, it is given a set of data points that have already been classified. The classifier then uses this data to learn the probability that a data point belongs to each class.

If a data point is missing some of the features that the classifier uses to make its predictions, the classifier will ignore those features and make its prediction based on the features that are available.

This is because the classifier does not have enough information to make a reliable prediction based on the missing features. By ignoring the missing features, the classifier can avoid making a prediction that is likely to be incorrect.

Here is a brief explanation of each option:

  • A. treated as equal compares. This is not correct because Bayes classifiers do not compare data points. They calculate the probability that a data point belongs to each class, and then assign the data point to the class with the highest probability.
  • B. treated as unequal compares. This is not correct because Bayes classifiers do not compare data points. They calculate the probability that a data point belongs to each class, and then assign the data point to the class with the highest probability.
  • C. replaced with a default value. This is not correct because Bayes classifiers do not replace missing data with default values. They ignore missing data and make their predictions based on the features that are available.
  • D. ignored. This is the correct answer because Bayes classifiers ignore missing data and make their predictions based on the features that are available.
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