Statements (136)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:machine_learning
|
| gptkbp:community |
open-source community
conferences and workshops active community active user community tutorials and resources support forums contributor community |
| gptkbp:contribution |
contributions from various developers
|
| gptkbp:dependency |
gptkb:matplotlib
gptkb:Sci_Py gptkb:Num_Py joblib threadpoolctl |
| gptkbp:developed_by |
gptkb:David_Cournapeau
|
| gptkbp:example |
available in documentation
|
| gptkbp:features |
gptkb:neural_networks
image processing data transformation time series analysis cross-validation text processing linear models outlier detection ensemble methods decision trees support vector machines feature extraction clustering algorithms grid search k-nearest neighbors metrics for evaluation data splitting Gaussian processes model persistence pipeline creation randomized search |
| gptkbp:first_released |
gptkb:2007
|
| gptkbp:has_documentation |
API reference
examples and tutorials https://scikit-learn.org/stable/documentation.html extensive user guide |
| https://www.w3.org/2000/01/rdf-schema#label |
scikit-learn
|
| gptkbp:integration |
compatible with Jupyter notebooks
compatible with Py Torch compatible with Tensor Flow compatible with pandas with Jupyter notebooks with matplotlib with pandas with seaborn |
| gptkbp:is_maintained_by |
scikit-learn developers
|
| gptkbp:language |
gptkb:Python
|
| gptkbp:latest_version |
1.1.0
1.2.0 1.3.0 1.0.2 |
| gptkbp:license |
gptkb:BSD_License
|
| gptkbp:notable_feature |
linear models
decision trees k-means clustering support vector machines data transformation tools neural network models feature extraction methods support for custom metrics time series analysis tools support for parallel processing support for various algorithms cross-validation tools grid search for hyperparameter tuning support for imbalanced datasets support for multi-label classification dimensionality reduction techniques like PCA ensemble methods like Random Forest feature importance evaluation metrics for model evaluation model persistence outlier detection methods pipeline for chaining estimators support for custom transformers support for multi-class classification |
| gptkbp:platform |
cross-platform
|
| gptkbp:programming_language |
gptkb:Python
|
| gptkbp:provides |
ensemble methods
decision trees k-means clustering support vector machines data transformation tools grid search principal component analysis text processing tools random forests metrics for evaluation time series analysis tools image processing tools cross-validation tools API for data preprocessing API for ensemble methods API for feature selection API for model evaluation API for pipeline creation feature extraction tools tools for data preprocessing tools for model evaluation API for machine learning algorithms pipeline tools tools for feature selection tools for hyperparameter tuning |
| gptkbp:release_date |
gptkb:2007
|
| gptkbp:repository |
https://github.com/scikit-learn/scikit-learn
|
| gptkbp:supports |
gptkb:Biology
model selection preprocessing dimensionality reduction regression clustering |
| gptkbp:tutorials |
available online
|
| gptkbp:type |
gptkb:open-source_software
|
| gptkbp:used_by |
gptkb:students
gptkb:researchers data scientists machine learning practitioners |
| gptkbp:used_in |
gptkb:academic_research
data science industry applications machine learning projects machine learning research academic projects |
| gptkbp:website |
https://scikit-learn.org
|
| gptkbp:written_in |
gptkb:Python
|
| gptkbp:bfsParent |
gptkb:Keras
gptkb:Random_Forests gptkb:Py_Torch gptkb:Oni |
| gptkbp:bfsLayer |
4
|