dashai-test-tabular-classification-package


Namedashai-test-tabular-classification-package JSON
Version 0.1.3 PyPI version JSON
download
home_pageNone
SummaryTabular Classification Package
upload_time2024-05-26 15:26:09
maintainerNone
docs_urlNone
authorDashAI team
requires_python>=3.8
licenseNone
keywords dashai dataloader model package task
VCS
bugtrack_url
requirements dependency-injector fastapi SQLAlchemy alembic numpy joblib pydantic pydantic-settings starlette scikit-learn datasets evaluate accelerate Pillow beartype typer rich torch transformers sacrebleu sentencepiece
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # **Tabular Classification Package**

## **Modelos**

Este conjunto de plugins está diseñado específicamente para facilitar la integración de modelos de Machine Learning en aplicaciones con enfoque en clasificación tabular. Los modelos incluidos son:

- **Logistic Regression:** Un modelo efectivo para abordar problemas de clasificación binaria en el contexto tabular, destacando por su simplicidad y rendimiento.
- **SVC (Support Vector Classifier):** Este clasificador basado en vectores de soporte se adapta bien a conjuntos de datos tabulares complejos, ofreciendo soluciones robustas tanto para clasificación como para regresión.
- **KNN:** Un modelo de clasificación basado en la proximidad de los datos, que se adapta bien a conjuntos de datos tabulares con una estructura clara y bien definida.
- **Random Forest:** Un modelo de clasificación basado en árboles de decisión, que destaca por su versatilidad y rendimiento en una amplia variedad de conjuntos de datos tabulares.

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "dashai-test-tabular-classification-package",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": null,
    "keywords": "DashAI, Dataloader, Model, Package, Task",
    "author": "DashAI team",
    "author_email": "dashaisoftware@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/83/71/3cd572b9b3abe5e4321cdcff7f26ecb136ce094b702ea8c2825dfb073ac9/dashai_test_tabular_classification_package-0.1.3.tar.gz",
    "platform": null,
    "description": "# **Tabular Classification Package**\n\n## **Modelos**\n\nEste conjunto de plugins est\u00e1 dise\u00f1ado espec\u00edficamente para facilitar la integraci\u00f3n de modelos de Machine Learning en aplicaciones con enfoque en clasificaci\u00f3n tabular. Los modelos incluidos son:\n\n- **Logistic Regression:** Un modelo efectivo para abordar problemas de clasificaci\u00f3n binaria en el contexto tabular, destacando por su simplicidad y rendimiento.\n- **SVC (Support Vector Classifier):** Este clasificador basado en vectores de soporte se adapta bien a conjuntos de datos tabulares complejos, ofreciendo soluciones robustas tanto para clasificaci\u00f3n como para regresi\u00f3n.\n- **KNN:** Un modelo de clasificaci\u00f3n basado en la proximidad de los datos, que se adapta bien a conjuntos de datos tabulares con una estructura clara y bien definida.\n- **Random Forest:** Un modelo de clasificaci\u00f3n basado en \u00e1rboles de decisi\u00f3n, que destaca por su versatilidad y rendimiento en una amplia variedad de conjuntos de datos tabulares.\n",
    "bugtrack_url": null,
    "license": null,
    "summary": "Tabular Classification Package",
    "version": "0.1.3",
    "project_urls": {
        "Homepage": "https://github.com/DashAISoftware/DashAI",
        "Issues": "https://github.com/DashAISoftware/DashAI/issues"
    },
    "split_keywords": [
        "dashai",
        " dataloader",
        " model",
        " package",
        " task"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "f84b3fe4296ce4be3f34b8644df9c2d8ed1ba6769eca6a20ff131c487f5a15ea",
                "md5": "b6d21c3f73dc2c22d69d73b6cf04a679",
                "sha256": "bc6604907d1a7c8869e866e5cafd3fb438c86f941989999193ea379cd5c57401"
            },
            "downloads": -1,
            "filename": "dashai_test_tabular_classification_package-0.1.3-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "b6d21c3f73dc2c22d69d73b6cf04a679",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.8",
            "size": 7793,
            "upload_time": "2024-05-26T15:26:07",
            "upload_time_iso_8601": "2024-05-26T15:26:07.878997Z",
            "url": "https://files.pythonhosted.org/packages/f8/4b/3fe4296ce4be3f34b8644df9c2d8ed1ba6769eca6a20ff131c487f5a15ea/dashai_test_tabular_classification_package-0.1.3-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "83713cd572b9b3abe5e4321cdcff7f26ecb136ce094b702ea8c2825dfb073ac9",
                "md5": "7b2322bbb0f272986ab5f60fd85e0b0c",
                "sha256": "cbee7ee35cfa4769b785fdc04b5b9af861d65a4039169d5a9d548031a955786e"
            },
            "downloads": -1,
            "filename": "dashai_test_tabular_classification_package-0.1.3.tar.gz",
            "has_sig": false,
            "md5_digest": "7b2322bbb0f272986ab5f60fd85e0b0c",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 5858,
            "upload_time": "2024-05-26T15:26:09",
            "upload_time_iso_8601": "2024-05-26T15:26:09.365380Z",
            "url": "https://files.pythonhosted.org/packages/83/71/3cd572b9b3abe5e4321cdcff7f26ecb136ce094b702ea8c2825dfb073ac9/dashai_test_tabular_classification_package-0.1.3.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-05-26 15:26:09",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "DashAISoftware",
    "github_project": "DashAI",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "requirements": [
        {
            "name": "dependency-injector",
            "specs": [
                [
                    ">=",
                    "4.0"
                ],
                [
                    "<",
                    "5.0"
                ]
            ]
        },
        {
            "name": "fastapi",
            "specs": [
                [
                    ">=",
                    "0.96"
                ]
            ]
        },
        {
            "name": "SQLAlchemy",
            "specs": [
                [
                    ">=",
                    "2.0"
                ]
            ]
        },
        {
            "name": "alembic",
            "specs": [
                [
                    "==",
                    "1.11.1"
                ]
            ]
        },
        {
            "name": "numpy",
            "specs": [
                [
                    ">=",
                    "1.17.3"
                ]
            ]
        },
        {
            "name": "joblib",
            "specs": [
                [
                    ">=",
                    "1.2.0"
                ]
            ]
        },
        {
            "name": "pydantic",
            "specs": [
                [
                    ">=",
                    "2.0.2"
                ]
            ]
        },
        {
            "name": "pydantic-settings",
            "specs": [
                [
                    ">=",
                    "2.0.1"
                ]
            ]
        },
        {
            "name": "starlette",
            "specs": [
                [
                    "<",
                    "0.28.0"
                ],
                [
                    ">=",
                    "0.27.0"
                ]
            ]
        },
        {
            "name": "scikit-learn",
            "specs": [
                [
                    ">=",
                    "1.2.1"
                ]
            ]
        },
        {
            "name": "datasets",
            "specs": [
                [
                    ">=",
                    "2.9.0"
                ]
            ]
        },
        {
            "name": "evaluate",
            "specs": [
                [
                    ">=",
                    "0.4.0"
                ]
            ]
        },
        {
            "name": "accelerate",
            "specs": [
                [
                    ">=",
                    "0.20.3"
                ]
            ]
        },
        {
            "name": "Pillow",
            "specs": [
                [
                    ">=",
                    "9.5.0"
                ]
            ]
        },
        {
            "name": "beartype",
            "specs": [
                [
                    "==",
                    "0.15.0"
                ]
            ]
        },
        {
            "name": "typer",
            "specs": [
                [
                    "==",
                    "0.9.0"
                ]
            ]
        },
        {
            "name": "rich",
            "specs": [
                [
                    ">=",
                    "13.5.3"
                ]
            ]
        },
        {
            "name": "torch",
            "specs": [
                [
                    "==",
                    "1.13.0"
                ]
            ]
        },
        {
            "name": "transformers",
            "specs": [
                [
                    "==",
                    "4.23.1"
                ]
            ]
        },
        {
            "name": "sacrebleu",
            "specs": [
                [
                    "==",
                    "2.3.1"
                ]
            ]
        },
        {
            "name": "sentencepiece",
            "specs": [
                [
                    "==",
                    "0.1.97"
                ]
            ]
        }
    ],
    "lcname": "dashai-test-tabular-classification-package"
}
        
Elapsed time: 0.26060s