"Markov" (Machine Learning Method)

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".1the smoothing parameter to use
    "MinimumTokenCount"Automaticminimum count for an n-gram to to be considered
    "Order"Automaticn-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.

Examples

open allclose all

Basic Examples  (1)

Train a classifier function on labeled examples:

In[1]:=
Click for copyable input
Out[1]=

Look at its ClassifierInformation:

In[2]:=
Click for copyable input
Out[2]=

Classify a new example:

In[3]:=
Click for copyable input
Out[3]=

Options  (4)

See Also

Classify  ClassifierFunction  ClassifierMeasurements  ClassifierInformation  Predict  SequencePredict  ClusterClassify

Related Methods