Automated Machine Learning (Auto ML)
GPTKB entity
Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software_framework
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:Neural_Architecture_Search
|
| gptkbp:benefits |
large enterprises
small businesses non-experts |
| gptkbp:can_lead_to |
better performance
cost savings faster deployment |
| gptkbp:challenges |
computational resources
data quality issues model interpretability |
| gptkbp:developed_by |
data scientists
|
| https://www.w3.org/2000/01/rdf-schema#label |
Automated Machine Learning (Auto ML)
|
| gptkbp:improves |
model selection
|
| gptkbp:includes |
gptkb:Auto-sklearn
gptkb:H2_O.ai gptkb:Google_Cloud_Auto_ML gptkb:TPOT Auto ML frameworks |
| gptkbp:is_adopted_by |
government agencies
research institutions startups |
| gptkbp:is_challenged_by |
ethical considerations
data privacy issues bias in algorithms |
| gptkbp:is_evaluated_by |
cross-validation
A/ B testing holdout validation |
| gptkbp:is_implemented_in |
gptkb:R
gptkb:Library |
| gptkbp:is_influenced_by |
user feedback
business objectives model performance metrics |
| gptkbp:is_popular_in |
gptkb:film_production_company
academia |
| gptkbp:is_promoted_by |
online courses
tech conferences data science communities |
| gptkbp:is_related_to |
gptkb:physicist
gptkb:Artificial_Intelligence |
| gptkbp:is_supported_by |
cloud platforms
commercial software open-source tools |
| gptkbp:is_used_in |
data preprocessing
hyperparameter tuning feature engineering |
| gptkbp:provides |
automated model evaluation
|
| gptkbp:purpose |
automate the process of applying machine learning to real-world problems
|
| gptkbp:reduces |
human intervention
|
| gptkbp:utilizes |
algorithms
|