Statements (46)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:machine_learning
|
| gptkbp:application |
financial forecasting
natural language processing image classification recommendation systems healthcare analytics |
| gptkbp:applies_to |
gptkb:Biology
time series forecasting regression |
| gptkbp:benefits |
improves accessibility of machine learning
reduces the need for expert knowledge speeds up the model development process |
| gptkbp:challenges |
requires large datasets
can produce overfitting may lack interpretability |
| gptkbp:developed_by |
gptkb:Google
|
| https://www.w3.org/2000/01/rdf-schema#label |
Auto ML
|
| gptkbp:includes |
hyperparameter tuning
model selection automated feature engineering |
| gptkbp:is_a_tool_for |
gptkb:H2_O.ai
gptkb:Auto_Keras gptkb:Data_Robot gptkb:Google_Cloud_Auto_ML gptkb:Microsoft_Azure_Auto_ML gptkb:TPOT |
| gptkbp:purpose |
automate machine learning tasks
|
| gptkbp:research_areas |
gptkb:machine_learning
data preprocessing model evaluation explainable AI scalable machine learning |
| gptkbp:technique |
gptkb:stage_adaptation
gptkb:Neural_Architecture_Search Ensemble Learning Meta-Learning |
| gptkbp:trends |
increased automation
improvements in user interfaces wider adoption in industries enhanced interpretability integration with big data |
| gptkbp:bfsParent |
gptkb:Google_Brain_team
gptkb:Google gptkb:Google_Brain gptkb:Neural_Architecture_Search |
| gptkbp:bfsLayer |
4
|