"Markov" (Machine Learning Method)
- Method for Classify.
- Model class probabilities using the n-gram frequencies of the given sequence.
Details & Suboptions
- In a Markov model, at training time, an n-gram language model is computed for each class. At test time, the probability for each class is computed according to Bayes's theorem, , where is given by the language model of the given class and is class prior.
- The following options can be given:
"AdditiveSmoothing" .1 the smoothing parameter to use "MinimumTokenCount" Automatic minimum count for an n-gram to to be considered "Order" Automatic n-gram length
- When "Order"n, the method partitions sequences in (n+1)-grams.
- When "Order"0, the method uses unigrams (single tokens). The model can then be called a unigram model or naive Bayes model.
- The value of "AdditiveSmoothing" is added to all n-gram counts. It is used to regularize the language model.
Examplesopen allclose all
Basic Examples (1)
Look at its ClassifierInformation: