Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Machine learning paradigm
|
| gptkbp:application |
Speech recognition
Credit scoring Image recognition Medical diagnosis Sentiment analysis Spam detection |
| gptkbp:assesses |
gptkb:Recall
gptkb:Mean_Squared_Error F1 score Precision Accuracy Area Under Curve |
| gptkbp:challenge |
Overfitting
Data labeling cost Imbalanced data Underfitting |
| gptkbp:contrastsWith |
Reinforcement learning
Unsupervised learning |
| gptkbp:dataRequirement |
Supervised dataset
|
| gptkbp:example |
Handwritten digit recognition
House price prediction |
| gptkbp:featureSelection |
Important for model performance
|
| gptkbp:goal |
Learn mapping from inputs to outputs
|
| https://www.w3.org/2000/01/rdf-schema#label |
Supervised learning
|
| gptkbp:hyperparameterTuning |
Improves model performance
|
| gptkbp:input |
Feature vector
|
| gptkbp:lossFunction |
Measures prediction error
|
| gptkbp:modelSelection |
Based on validation set
|
| gptkbp:originatedIn |
Statistics
Pattern recognition |
| gptkbp:output |
Label
Continuous value Discrete class |
| gptkbp:relatedConcept |
Active learning
Transfer learning Semi-supervised learning |
| gptkbp:requires |
Labeled data
|
| gptkbp:supportsAlgorithm |
gptkb:Support_Vector_Machine
gptkb:Linear_Regression gptkb:Random_Forest gptkb:Neural_Network Logistic Regression k-Nearest Neighbors Decision Tree |
| gptkbp:testProcess |
Model predicts labels for unseen data
|
| gptkbp:trainingProcess |
Model learns from labeled examples
|
| gptkbp:usedIn |
Regression
Classification |
| gptkbp:bfsParent |
gptkb:PAC_learning
|
| gptkbp:bfsLayer |
5
|