# spatial-kfold
spatial resampling for more robust cross validation in spatial studies
spatial-kfold is a python library for performing spatial resampling to ensure more robust cross-validation in spatial studies. It offers spatial clustering and block resampling technique with user-friendly parameters to customize the resampling. It enables users to conduct a "Leave Region Out" cross-validation, which can be useful for evaluating the model's generalization to new locations as well as improving the reliability of [feature selection](https://doi.org/10.1016/j.ecolmodel.2019.108815) and [hyperparameter tuning](https://doi.org/10.1016/j.ecolmodel.2019.06.002) in spatial studies
Raw data
{
"_id": null,
"home_page": "https://github.com/WalidGharianiEAGLE/spatial-kfold",
"name": "gbtm-tpom",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "spatial",
"author": "Walid Ghariani",
"author_email": "walid11ghariani@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/58/2e/620634a51e0f571468bdaa30ac6d9ee238b43deb179da871c3eb950c0d89/gbtm-tpom-0.0.1.tar.gz",
"platform": null,
"description": "# spatial-kfold\nspatial resampling for more robust cross validation in spatial studies\n\nspatial-kfold is a python library for performing spatial resampling to ensure more robust cross-validation in spatial studies. It offers spatial clustering and block resampling technique with user-friendly parameters to customize the resampling. It enables users to conduct a \"Leave Region Out\" cross-validation, which can be useful for evaluating the model's generalization to new locations as well as improving the reliability of [feature selection](https://doi.org/10.1016/j.ecolmodel.2019.108815) and [hyperparameter tuning](https://doi.org/10.1016/j.ecolmodel.2019.06.002) in spatial studies\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "wood class",
"version": "0.0.1",
"split_keywords": [
"spatial"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4251ada9ba83464c217004382eb06bd04800c684746faf782321ac94b5269026",
"md5": "118be53671a155acd05ee778c51882f8",
"sha256": "1b06c151b11687f494de6226449af3167061074cf006e2e511bd9b6c24895ca6"
},
"downloads": -1,
"filename": "gbtm_tpom-0.0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "118be53671a155acd05ee778c51882f8",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 272679,
"upload_time": "2023-03-20T10:14:35",
"upload_time_iso_8601": "2023-03-20T10:14:35.537352Z",
"url": "https://files.pythonhosted.org/packages/42/51/ada9ba83464c217004382eb06bd04800c684746faf782321ac94b5269026/gbtm_tpom-0.0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "582e620634a51e0f571468bdaa30ac6d9ee238b43deb179da871c3eb950c0d89",
"md5": "b864d4d5b1edfb3f12bc637fce95bcec",
"sha256": "fbf9773ec878130f433b1345b4630f47336b0da50b85db0d7bd556e0da6a0f28"
},
"downloads": -1,
"filename": "gbtm-tpom-0.0.1.tar.gz",
"has_sig": false,
"md5_digest": "b864d4d5b1edfb3f12bc637fce95bcec",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 245882,
"upload_time": "2023-03-20T10:14:38",
"upload_time_iso_8601": "2023-03-20T10:14:38.915721Z",
"url": "https://files.pythonhosted.org/packages/58/2e/620634a51e0f571468bdaa30ac6d9ee238b43deb179da871c3eb950c0d89/gbtm-tpom-0.0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-03-20 10:14:38",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "WalidGharianiEAGLE",
"github_project": "spatial-kfold",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "pandas",
"specs": [
[
">=",
"0.20"
]
]
},
{
"name": "numpy",
"specs": [
[
">=",
"1.10.0"
]
]
},
{
"name": "geopandas",
"specs": [
[
">=",
"0.3"
]
]
},
{
"name": "shapely",
"specs": [
[
">=",
"1.6"
]
]
},
{
"name": "matplotlib",
"specs": [
[
">=",
"2.2.4"
]
]
},
{
"name": "scikit-learn",
"specs": [
[
">=",
"0.22"
]
]
}
],
"lcname": "gbtm-tpom"
}
JSON
