Statements (56)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software_framework
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:Automated_Machine_Learning_(Auto_ML)
|
| gptkbp:allows |
requires significant computational resources
can be sensitive to data quality may not perform well on small datasets requires careful tuning for optimal performance |
| gptkbp:based_on |
Bayesian optimization
|
| gptkbp:developed_by |
Fraunhofer UMSICHT
|
| gptkbp:has_community |
active user community
|
| gptkbp:has_documentation |
https://automl.github.io/auto-sklearn/master/
|
| gptkbp:has_feature |
data preprocessing
model evaluation automated pipeline generation ensemble construction |
| https://www.w3.org/2000/01/rdf-schema#label |
Auto-sklearn
|
| gptkbp:is_a_hub_for |
https://github.com/automl/auto-sklearn
|
| gptkbp:is_available_on |
gptkb:Anaconda
gptkb:Py_PI |
| gptkbp:is_compatible_with |
gptkb:lake
gptkb:Google_Colab gptkb:computer |
| gptkbp:is_influenced_by |
gptkb:H2_O.ai
gptkb:TPOT Auto-WEKA |
| gptkbp:is_optimized_for |
model selection
hyperparameters feature preprocessing |
| gptkbp:is_part_of |
gptkb:Open_ML
Auto ML framework |
| gptkbp:is_related_to |
gptkb:Artificial_Intelligence
gptkb:software_framework data mining predictive modeling data preprocessing techniques |
| gptkbp:is_used_by |
gptkb:University
gptkb:physicist data analysts industry professionals |
| gptkbp:is_used_in |
data science
machine learning competitions |
| gptkbp:language |
gptkb:Library
|
| gptkbp:performance |
F1 score
accuracy AUC mean squared error |
| gptkbp:provides |
hyperparameter optimization
automated model selection |
| gptkbp:release_date |
gptkb:2015
|
| gptkbp:requires |
Python version 3.5 or higher
scikit-learn version 0.18 or higher |
| gptkbp:supports |
regression
multi-class classification ensemble learning |
| gptkbp:uses |
gptkb:scikit-learn
meta-learning |