Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
machine learning technology
|
| gptkbp:challenge |
model interpretability
data quality computational cost search space complexity |
| gptkbp:enables |
non-experts to build ML models
rapid prototyping of ML solutions |
| gptkbp:fieldOfStudy |
gptkb:artificial_intelligence
gptkb:machine_learning data science |
| gptkbp:goal |
democratize machine learning
improve efficiency of ML model development reduce human intervention in ML pipeline |
| https://www.w3.org/2000/01/rdf-schema#label |
AutoML models
|
| gptkbp:implementedIn |
gptkb:DataRobot
gptkb:H2O_AutoML gptkb:Amazon_SageMaker_Autopilot gptkb:Auto-sklearn gptkb:IBM_AutoAI gptkb:Microsoft_Azure_AutoML gptkb:TPOT gptkb:Google_AutoML gptkb:MLJAR_AutoML AutoGluon |
| gptkbp:originatedIn |
research in hyperparameter optimization
research in meta-learning research in neural architecture search |
| gptkbp:output |
feature importance
model explanations model performance metrics trained machine learning models |
| gptkbp:popularizedBy |
gptkb:Auto-sklearn
gptkb:TPOT gptkb:Google_AutoML |
| gptkbp:relatedTo |
automated machine learning
|
| gptkbp:supports |
gptkb:dictionary
regression clustering time series forecasting tabular data image data text data |
| gptkbp:usedBy |
business analysts
data scientists machine learning engineers |
| gptkbp:usedFor |
automating data preprocessing
automating feature engineering automating hyperparameter tuning automating model evaluation automating model selection |
| gptkbp:bfsParent |
gptkb:Vertex_AI_Prediction
gptkb:Vertex_Explainable_AI |
| gptkbp:bfsLayer |
6
|