|
gptkbp:instanceOf
|
gptkb:model
classifier
|
|
gptkbp:advantage
|
fast training
simple implementation
works with small data
not suitable for correlated features
strong independence assumption
|
|
gptkbp:assumes
|
feature independence
|
|
gptkbp:basedOn
|
gptkb:Bayes'_theorem
|
|
gptkbp:canBe
|
high-dimensional data
|
|
gptkbp:cannotHandle
|
dependent features
|
|
gptkbp:category
|
supervised learning
statistical classification
|
|
gptkbp:commonDataset
|
gptkb:20_Newsgroups
gptkb:Iris_dataset
gptkb:SMS_Spam_Collection
|
|
gptkbp:compatibleWith
|
large computational resources
|
|
gptkbp:firstDescribed
|
1960s
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Naive Bayes
|
|
gptkbp:implementedIn
|
gptkb:Spark_MLlib
gptkb:Weka
gptkb:scikit-learn
R
|
|
gptkbp:input
|
feature vector
|
|
gptkbp:output
|
gptkb:organization
class label
|
|
gptkbp:predictionComplexity
|
linear
|
|
gptkbp:relatedTo
|
Bayesian statistics
decision trees
logistic regression
|
|
gptkbp:requires
|
labeled data
|
|
gptkbp:trainingComplexity
|
linear
|
|
gptkbp:type
|
gptkb:model
|
|
gptkbp:usedFor
|
gptkb:dictionary
spam filtering
sentiment analysis
text classification
|
|
gptkbp:usedIn
|
natural language processing
diagnosis
recommender systems
|
|
gptkbp:variant
|
gptkb:Bernoulli_Naive_Bayes
gptkb:Gaussian_Naive_Bayes
gptkb:Multinomial_Naive_Bayes
|
|
gptkbp:bfsParent
|
gptkb:Statistics_and_Machine_Learning_Toolbox
gptkb:Text_Classification
gptkb:Text_Mining
|
|
gptkbp:bfsLayer
|
6
|