Naïve Bayes Classifier

35 min
Block 11 — Classification algorithms
Objective
state Bayes' theorem precisely with its four components, perform a posterior probability calculation on a low-prevalence case, derive the maximum a posteriori decision rule, define the conditional independence assumption and explain why a model built on a false assumption remains accurate in ranking but not in probability calibration, select the appropriate variant for the data type, and apply additive smoothing and logarithmic form in implementation.
Estimated duration
45 minutes
Prerequisites
chapters 004, 010, 011, 029, and 030
Associated quizzes
042.1-bayes-theorem.md to 042.7-strengths-limitations.md

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This lesson is part of the “Classification Algorithms” module

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