|
gptkbp:instanceOf
|
gptkb:model
|
|
gptkbp:category
|
gptkb:artificial_intelligence
gptkb:software
data analysis
statistics
|
|
gptkbp:citation
|
Pedregosa et al., JMLR 12, pp. 2825-2830, 2011
|
|
gptkbp:dependsOn
|
gptkb:NumPy
gptkb:SciPy
gptkb:joblib
gptkb:threadpoolctl
|
|
gptkbp:developer
|
gptkb:David_Cournapeau
|
|
gptkbp:documentation
|
https://scikit-learn.org/stable/documentation.html
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Scikit-learn
|
|
gptkbp:latestReleaseVersion
|
1.4.2
|
|
gptkbp:license
|
gptkb:BSD_license
|
|
gptkbp:logo
|
https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png
|
|
gptkbp:maintainedBy
|
gptkb:Scikit-learn_developers
|
|
gptkbp:officialWebsite
|
https://scikit-learn.org/
|
|
gptkbp:partOf
|
Python scientific stack
|
|
gptkbp:platform
|
Cross-platform
|
|
gptkbp:releaseDate
|
2007
|
|
gptkbp:repository
|
https://github.com/scikit-learn/scikit-learn
|
|
gptkbp:supportsAlgorithm
|
gptkb:principal_component_analysis
gptkb:AdaBoost
gptkb:DBSCAN
gptkb:Gaussian_mixture_models
gptkb:Lasso_regression
gptkb:naive_Bayes
pipeline
cross-validation
decision trees
ensemble methods
feature selection
gradient boosting
grid search
k-means clustering
k-nearest neighbors
linear regression
logistic regression
random forests
support vector machines
ridge regression
|
|
gptkbp:usedBy
|
gptkb:researchers
students
data scientists
machine learning engineers
|
|
gptkbp:usedFor
|
gptkb:dictionary
regression
clustering
dimensionality reduction
model selection
preprocessing
|
|
gptkbp:writtenBy
|
gptkb:Python
|
|
gptkbp:bfsParent
|
gptkb:artificial_intelligence
gptkb:software
|
|
gptkbp:bfsLayer
|
4
|