So the output is a probability of belonging to a class. Decision tree vs. The Naive Bayes classifier approximates the Optimal Bayes classifier by looking at the empirical distribution and by assuming conditional independence of explanatory variables, given a class. Unlike Bayes and K-NN, decision trees can work directly from a table of data, without any prior design work. If training data is much larger than no. Support Vector Machine (SVM) is better at full-length content. Naive bayes is parametric whereas KNN is non-parametric. KNN vs linear regression : KNN is better than linear regression when the data have high SNR. A 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. MNB is stronger for snippets than for longer documents. 4.

Naive Bayes comes under the class of generative models for classification. So the Naive Bayes classifier is not itself optimal, but it approximates the optimal solution. KNN vs SVM : SVM take cares of outliers better than KNN. Naive bayes is much faster than KNN due to KNN’s real-time execution. While (Ng and Jordan, 2002) showed that NB is better than SVM/logistic regression (LR) with few training cases, MNB is also better with short documents. Multinomial Naive Bayes (MNB) is better at snippets. Naive Bayes … References. 5. Naive Bayes requires you to know your classifiers in advance. If you don't know your classifiers, a decision tree will choose those classifiers for you from a data table. It models the posterior probability from the class conditional densities.


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