What is the relationship between KNN and the Bayes optimal classifier?
The basic difference between K-NN classifier and Naive Bayes classifier is that, the former is a discriminative classifier but the latter is a generative classifier. Going into specifics, K-NN classifier is a supervised lazy classifier which has local heuristics.
Which is faster kNN or Naive Bayes?
Naive Bayes is a linear classifier while K-NN is not; It tends to be faster when applied to big data. In comparison, k-nn is usually slower for large amounts of data, because of the calculations required for each new step in the process. In general, Naive Bayes is highly accurate when applied to big data.
How is vector space classification used in naive Bayes?
Vector space classification The document representation in Naive Bayes is a sequence of terms or a binary vector . In this chapter we adopt a different representation for text classification, the vector space model, developed in Chapter 6.
Which is faster k-NN or naive Bayes?
Naive Bayes is an eager learning classifier and it is much faster than K-NN. Thus, it could be used for prediction in real time. Typically, email spam filtering uses Naive Bayes classifier.
How is a document represented in naive Bayes?
The document representation in Naive Bayes is a sequence of terms or a binary vector . In this chapter we adopt a different representation for text classification, the vector space model, developed in Chapter 6. It represents each document as a vector with one real-valued component, usually a tf-idf weight, for each term.
Why is the naive Bayes algorithm unrealistic?
Naive Bayes requires that you known the underlying probability distributions for categories. The algorithm compares all other classifiers against this ideal. Therefore, unless you know the probabilities and pdfs, use of the ideal Bayes is unrealistic.