Statements (60)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:computer
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:Naive_Bayes
|
| gptkbp:applies_to |
categorical data
|
| gptkbp:based_on |
Bayes' theorem
word frequency |
| gptkbp:can_be_extended_by |
multiclass problems
|
| gptkbp:can_be_used_with |
feature selection techniques
|
| gptkbp:controls |
multiple classes
|
| gptkbp:has_impact_on |
features are conditionally independent
|
| https://www.w3.org/2000/01/rdf-schema#label |
Multinomial Naive Bayes
|
| gptkbp:input_output |
probabilities
|
| gptkbp:is_a |
probabilistic classifier
|
| gptkbp:is_effective_against |
data is sparse
features are correlated features are not independent |
| gptkbp:is_evaluated_by |
accuracy metrics
F1 score ROC curve confusion matrix cross-validation precision metrics |
| gptkbp:is_implemented_in |
gptkb:R
gptkb:scikit-learn |
| gptkbp:is_influenced_by |
prior probabilities
|
| gptkbp:is_often_used_in |
gptkb:software_framework
gptkb:computer data science AI applications |
| gptkbp:is_popular_in |
natural language processing
text categorization |
| gptkbp:is_related_to |
data preprocessing
data mining feature engineering feature extraction text mining text analytics |
| gptkbp:is_used_for |
spam detection
text classification |
| gptkbp:is_used_in |
language detection
sentiment analysis topic modeling recommendation systems search engine optimization customer feedback analysis social media analysis email filtering content categorization news categorization product review classification user intent classification |
| gptkbp:relationship |
gptkb:microprocessor
|
| gptkbp:requires |
discrete features
|
| gptkbp:security_features |
overfitting
|
| gptkbp:sensor |
class imbalance
|
| gptkbp:speed |
gptkb:SVM
|
| gptkbp:suitable_for |
continuous data
real-time predictions |
| gptkbp:training |
maximum likelihood estimation
|
| gptkbp:works_with |
large datasets
|