Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Biology
|
| gptkbp:applies_to |
binary features
|
| gptkbp:can_be_combined_with |
feature selection techniques
|
| gptkbp:can_be_used_with |
bag-of-words model
|
| gptkbp:can_handle |
missing data
|
| gptkbp:has_function |
alpha
|
| https://www.w3.org/2000/01/rdf-schema#label |
Bernoulli Naive Bayes
|
| gptkbp:input_output |
class probabilities
|
| gptkbp:is_applied_in |
gptkb:Biology
|
| gptkbp:is_based_on |
Bayes' theorem
independence assumption |
| gptkbp:is_evaluated_by |
gptkb:historical_memory
F1 score ROC curve accuracy cross-validation precision |
| gptkbp:is_implemented_in |
gptkb:R
gptkb:scikit-learn |
| gptkbp:is_often_used_in |
natural language processing
|
| gptkbp:is_part_of |
supervised learning
ensemble methods |
| gptkbp:is_popular_in |
data science
|
| gptkbp:is_related_to |
gptkb:machine_learning
|
| gptkbp:is_similar_to |
gptkb:Multinomial_Naive_Bayes
|
| gptkbp:is_trained_in |
training dataset
|
| gptkbp:is_used_for |
sentiment analysis
spam detection text mining |
| gptkbp:is_used_in |
gptkb:recommendations
risk assessment language detection topic modeling customer segmentation fraud detection image classification recommendation systems medical diagnosis customer feedback analysis ad targeting text summarization social media analysis content recommendation text classification email filtering market basket analysis news categorization user behavior prediction |
| gptkbp:requires |
feature extraction
|
| gptkbp:security |
overfitting
|
| gptkbp:sensitivity |
class imbalance
|
| gptkbp:speed |
in training
|
| gptkbp:suitable_for |
continuous data
high-dimensional data |
| gptkbp:training |
maximum likelihood estimation
|
| gptkbp:uses |
gptkb:Bernoulli_distribution
|
| gptkbp:bfsParent |
gptkb:Naive_Bayes
|
| gptkbp:bfsLayer |
5
|