Statements (57)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:Auto_ML
|
| gptkbp:community |
open-source
|
| gptkbp:community_support |
active community
|
| gptkbp:dependency |
gptkb:tqdm
gptkb:Sci_Py gptkb:Num_Py pandas joblib deap |
| gptkbp:developed_by |
Epistasis Lab
|
| gptkbp:features |
genetic programming
|
| gptkbp:goal |
automate the process of machine learning model selection and hyperparameter tuning
|
| gptkbp:has |
available in documentation
|
| gptkbp:has_documentation |
available online
|
| https://www.w3.org/2000/01/rdf-schema#label |
TPOT
|
| gptkbp:input_output |
pandas Data Frame
scikit-learn pipeline |
| gptkbp:installation |
pip install
|
| gptkbp:integrates_with |
gptkb:Google_Colab
gptkb:computer gptkb:Kaggle |
| gptkbp:is_compatible_with |
gptkb:smartphone
gptkb:operating_system Python 3.6+ |
| gptkbp:is_used_in |
data science projects
research projects machine learning competitions |
| gptkbp:issues |
tracked on Git Hub
|
| gptkbp:language |
gptkb:Library
|
| gptkbp:latest_version |
1.0.12
|
| gptkbp:license |
Apache License 2.0
|
| gptkbp:notable_recipients |
multiple contributors
|
| gptkbp:performance |
F1 score
accuracy R-squared mean squared error ROCAUC |
| gptkbp:provides |
visualization tools
model evaluation ensemble methods automated feature engineering pipeline optimization |
| gptkbp:release_year |
gptkb:2017
|
| gptkbp:released |
frequent updates
|
| gptkbp:repository |
gptkb:archive
|
| gptkbp:supports |
gptkb:computer
binary classification time series forecasting regression multi-class classification |
| gptkbp:tutorials |
available online
|
| gptkbp:type |
gptkb:software_framework
|
| gptkbp:uses |
gptkb:scikit-learn
cross-validation genetic algorithms |