Tabular Regression
Scikit-learn
English
scikit-learn
linear-regression
microsoft-fabric
mlflow
diabetes
healthcare
Instructions to use kendrickfff/Disease-Progression-Prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use kendrickfff/Disease-Progression-Prediction with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("kendrickfff/Disease-Progression-Prediction", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Upload 10 files
Browse files- MLmodel +34 -0
- conda.yaml +14 -0
- input_example.json +1 -0
- metadata/MLmodel +34 -0
- metadata/conda.yaml +14 -0
- metadata/python_env.yaml +7 -0
- metadata/requirements.txt +7 -0
- model.pkl +3 -0
- python_env.yaml +7 -0
- requirements.txt +7 -0
MLmodel
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artifact_path: model
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flavors:
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python_function:
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env:
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conda: conda.yaml
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virtualenv: python_env.yaml
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loader_module: mlflow.sklearn
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model_path: model.pkl
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predict_fn: predict
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python_version: 3.11.8
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sklearn:
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code: null
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pickled_model: model.pkl
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serialization_format: cloudpickle
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sklearn_version: 1.2.2
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mlflow_version: 2.12.2
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model_size_bytes: 1257
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model_uuid: dc17d9ee06a94fcd9e0f9a1e962736f3
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run_id: d8f52d42-3e09-4f71-8747-6667f3460ba2
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saved_input_example_info:
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artifact_path: input_example.json
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pandas_orient: split
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type: dataframe
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signature:
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inputs: '[{"type": "long", "name": "AGE", "required": true}, {"type": "long", "name":
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"SEX", "required": true}, {"type": "double", "name": "BMI", "required": true},
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{"type": "double", "name": "BP", "required": true}, {"type": "long", "name": "S1",
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"required": true}, {"type": "double", "name": "S2", "required": true}, {"type":
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"double", "name": "S3", "required": true}, {"type": "double", "name": "S4", "required":
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true}, {"type": "double", "name": "S5", "required": true}, {"type": "long", "name":
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"S6", "required": true}]'
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outputs: '[{"type": "long", "name": "Risk_predicted", "required": true}]'
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params: null
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utc_time_created: '2026-02-18 15:11:23.832924'
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conda.yaml
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channels:
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- conda-forge
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dependencies:
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- python=3.11.8
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- pip<=24.0
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- pip:
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- mlflow==2.12.2
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- cloudpickle==2.2.1
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- configparser==5.0.2
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- lz4==4.3.2
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- psutil==5.9.0
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- scikit-learn==1.2.2
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- typing-extensions==4.9.0
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name: mlflow-env
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input_example.json
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{"columns": ["AGE", "SEX", "BMI", "BP", "S1", "S2", "S3", "S4", "S5", "S6"], "data": [[54.0, 2.0, 36.1, 115.0, 163.0, 98.4, 43.0, 4.0, 4.6821, 101.0], [45.0, 1.0, 32.0, 110.0, 224.0, 134.2, 45.0, 5.0, 5.4116, 93.0], [59.0, 2.0, 27.2, 107.0, 158.0, 102.0, 39.0, 4.0, 4.4427, 93.0], [45.0, 1.0, 24.2, 83.0, 177.0, 118.4, 45.0, 4.0, 4.2195, 82.0], [42.0, 1.0, 30.6, 121.0, 176.0, 92.8, 69.0, 3.0, 4.2627, 89.0]]}
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metadata/MLmodel
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artifact_path: model
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flavors:
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python_function:
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env:
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conda: conda.yaml
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virtualenv: python_env.yaml
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loader_module: mlflow.sklearn
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model_path: model.pkl
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predict_fn: predict
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python_version: 3.11.8
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sklearn:
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code: null
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pickled_model: model.pkl
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serialization_format: cloudpickle
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sklearn_version: 1.2.2
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mlflow_version: 2.12.2
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model_size_bytes: 1257
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model_uuid: dc17d9ee06a94fcd9e0f9a1e962736f3
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run_id: d8f52d42-3e09-4f71-8747-6667f3460ba2
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saved_input_example_info:
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artifact_path: input_example.json
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pandas_orient: split
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type: dataframe
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signature:
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inputs: '[{"type": "long", "name": "AGE", "required": true}, {"type": "long", "name":
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"SEX", "required": true}, {"type": "double", "name": "BMI", "required": true},
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{"type": "double", "name": "BP", "required": true}, {"type": "long", "name": "S1",
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"required": true}, {"type": "double", "name": "S2", "required": true}, {"type":
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"double", "name": "S3", "required": true}, {"type": "double", "name": "S4", "required":
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true}, {"type": "double", "name": "S5", "required": true}, {"type": "long", "name":
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"S6", "required": true}]'
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outputs: '[{"type": "long", "name": "Risk_predicted", "required": true}]'
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params: null
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utc_time_created: '2026-02-18 15:11:23.832924'
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metadata/conda.yaml
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channels:
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- conda-forge
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dependencies:
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- python=3.11.8
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- pip<=24.0
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- pip:
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- mlflow==2.12.2
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- cloudpickle==2.2.1
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- configparser==5.0.2
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- lz4==4.3.2
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- psutil==5.9.0
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- scikit-learn==1.2.2
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- typing-extensions==4.9.0
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name: mlflow-env
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metadata/python_env.yaml
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python: 3.11.8
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build_dependencies:
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- pip==24.0
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- setuptools==68.2.2
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- wheel==0.41.2
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dependencies:
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- -r requirements.txt
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metadata/requirements.txt
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mlflow==2.12.2
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cloudpickle==2.2.1
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configparser==5.0.2
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lz4==4.3.2
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psutil==5.9.0
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scikit-learn==1.2.2
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typing-extensions==4.9.0
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model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:7b3cf4d28b47c4d2a3eecf9e1075ca390ffa80ec6572f3fb60e62136cd35346c
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size 848
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python_env.yaml
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python: 3.11.8
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build_dependencies:
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- pip==24.0
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- setuptools==68.2.2
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- wheel==0.41.2
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dependencies:
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- -r requirements.txt
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requirements.txt
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mlflow==2.12.2
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cloudpickle==2.2.1
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configparser==5.0.2
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lz4==4.3.2
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psutil==5.9.0
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scikit-learn==1.2.2
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typing-extensions==4.9.0
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