Safe Haskell | None |
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Bayesian classification is one of the standard algorithms in machine learning. Typically, we make the naive bayes assumption of assuming that none of our attributes are correlated. The Bayes data type, however, is capable of both naive and non-naive assumptions.

# Documentation

Eq dist => Eq (Bayes label dist) | |

Ord dist => Ord (Bayes label dist) | |

Read dist => Read (Bayes label dist) | |

Show dist => Show (Bayes label dist) | |

Monoid dist => Monoid (Bayes label dist) | |

(Monoid dist, HomTrainer dist) => HomTrainer (Bayes label dist) | |

Abelian dist => Abelian (Bayes label dist) | |

Group dist => Group (Bayes label dist) | |

Probabilistic (Bayes labelLens dist) | |

(ProbabilityClassifier (Bayes labelLens dist), ~ * (Label (Datapoint (Bayes labelLens dist))) (Datapoint (Margin labelLens dist)), Mean (Margin labelLens dist)) => Classifier (Bayes labelLens dist) | |

(~ * (Margin labelLens dist) (Categorical label prob), Ord label, Ord prob, Fractional prob, ~ * label (Label (Datapoint dist)), ~ * prob (Probability (MarginalizeOut labelLens dist)), Labeled (Datapoint dist), ~ * (Datapoint (MarginalizeOut labelLens dist)) (Attributes (Datapoint dist)), PDF (MarginalizeOut labelLens dist), PDF (Margin labelLens dist), Marginalize labelLens dist) => ProbabilityClassifier (Bayes labelLens dist) |