Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
machine learning technique
|
| gptkbp:application |
speech recognition
diagnosis fraud detection recommendation systems financial modeling risk assessment image classification sentiment analysis credit scoring anomaly detection predictive maintenance supply chain optimization object detection time series forecasting text classification customer segmentation marketing analytics tabular data analysis demand forecasting churn prediction disease outbreak prediction energy consumption prediction |
| gptkbp:challenge |
scalability
model interpretability computational cost |
| gptkbp:enables |
non-experts to use machine learning
|
| gptkbp:firstMentioned |
2015
|
| gptkbp:fullName |
gptkb:Automated_Machine_Learning
|
| gptkbp:goal |
reduce human intervention in machine learning pipeline
|
| https://www.w3.org/2000/01/rdf-schema#label |
autoML
|
| gptkbp:includes |
data preprocessing
feature engineering model selection model evaluation hyperparameter optimization |
| gptkbp:popularLibraries |
gptkb:Google_Cloud_AutoML
gptkb:H2O_AutoML gptkb:Amazon_SageMaker_Autopilot gptkb:Auto-sklearn gptkb:Microsoft_Azure_AutoML gptkb:TPOT |
| gptkbp:purpose |
automate end-to-end process of applying machine learning to real-world problems
|
| gptkbp:relatedTo |
hyperparameter tuning
meta-learning neural architecture search |
| gptkbp:usedBy |
business analysts
data scientists machine learning engineers |
| gptkbp:bfsParent |
gptkb:H2O-3
|
| gptkbp:bfsLayer |
6
|