Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
statistical classifier |
| gptkbp:advantage |
fast training
simple implementation may perform poorly with correlated features works with small data strong independence assumption |
| gptkbp:appliesTo |
information retrieval
diagnosis recommender systems |
| gptkbp:assumes |
feature independence
|
| gptkbp:basedOn |
gptkb:Bayes'_theorem
|
| gptkbp:category |
supervised learning
|
| gptkbp:feature |
scalability
handles missing data robust to irrelevant features works with categorical data works with continuous data |
| https://www.w3.org/2000/01/rdf-schema#label |
Naive Bayes classifier
|
| gptkbp:implementedIn |
gptkb:Weka
gptkb:scikit-learn R |
| gptkbp:introducedIn |
1960s
|
| gptkbp:limitation |
zero probability problem
|
| gptkbp:output |
gptkb:organization
class label |
| gptkbp:relatedTo |
gptkb:tree
support vector machine logistic regression |
| gptkbp:requires |
gptkb:law
likelihood posterior probability prior probability |
| gptkbp:solvedBy |
Laplace smoothing
additive smoothing |
| gptkbp:testingComplexity |
linear
|
| gptkbp:trainingComplexity |
linear
|
| gptkbp:type |
gptkb:model
generative model |
| gptkbp:usedFor |
gptkb:dictionary
spam filtering sentiment analysis document classification text categorization |
| gptkbp:variant |
gptkb:Bernoulli_Naive_Bayes
gptkb:Gaussian_Naive_Bayes gptkb:Multinomial_Naive_Bayes |
| gptkbp:bfsParent |
gptkb:Bayes_Theory
gptkb:Multinomial_distribution gptkb:Bayesian_Network |
| gptkbp:bfsLayer |
7
|