|
gptkbp:instanceOf
|
gptkb:model
gptkb:statistical_classifier
|
|
gptkbp:advantage
|
fast training
simple implementation
may perform poorly with correlated features
works with small data
strong independence assumption
|
|
gptkbp:appliesTo
|
gptkb:diagnosis
information retrieval
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
|
|
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
gptkb: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
|
|
gptkbp:bfsLayer
|
7
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Naive Bayes classifier
|