|
gptkbp:instance_of
|
gptkb:software
|
|
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:Kaggle
gptkb:Jupyter_Notebook
|
|
gptkbp:is_compatible_with
|
gptkb:Linux
gptkb:mac_OS
gptkb:Windows
Python 3.6+
|
|
gptkbp:is_used_in
|
data science projects
research projects
machine learning competitions
|
|
gptkbp:issues
|
tracked on Git Hub
|
|
gptkbp:language
|
gptkb:Python
|
|
gptkbp:latest_version
|
1.0.12
|
|
gptkbp:license
|
Apache License 2.0
|
|
gptkbp:notable_contributor
|
multiple contributors
|
|
gptkbp:performance
|
F1 score
ROC AUC
accuracy
R-squared
mean squared error
|
|
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:Git_Hub
|
|
gptkbp:supports
|
gptkb:Biology
binary classification
time series forecasting
regression
multi-class classification
|
|
gptkbp:tutorials
|
available online
|
|
gptkbp:type
|
gptkb:machine_learning
|
|
gptkbp:uses
|
gptkb:scikit-learn
cross-validation
genetic algorithms
|
|
gptkbp:bfsParent
|
gptkb:Automated_Machine_Learning_(Auto_ML)
gptkb:Auto_ML
|
|
gptkbp:bfsLayer
|
5
|