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--- |
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library_name: sklearn |
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tags: |
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- tabular-regression |
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- machine-learning |
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- civil-engineering |
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datasets: |
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- custom |
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license: mit |
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--- |
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# Model Name: c3cf |
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## Model Description |
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**c3cf**, Cascade Forest models for predicting the Compressive strength of Coal-ash-incorporated Cement composites, were developed in the research article: [Coal ashes as supplementary cementitious materials: physicochemical property effects on hydration and strength, along with property-informed machine learning modeling](). They are tree-based ensemble models that implements the [deep forest](https://github.com/LAMDA-NJU/Deep-Forest) algorithm. |
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- **Developed by:** Kangyi Cai @ Missouri S&T |
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- **Model type:** Cascade Forest |
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- **Language(s):** Python |
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- **License:** MIT |
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## Uses & Limitations |
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**c3cf** can make reasonable predictions for coal-ash-incorporated cement mortars, whose strength is in the range of 15-65 MPa, replacement level of coal ash is <50% by mass, and curing age is between 7 and 91 days. |
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## How to Get Started with the Model |
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This repository contains a collection of regression models located in the `regs/` directory. Refer to the [kycai/c3cf](https://github.com/kycai/c3cf) for detailed guides. |
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```python |
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from huggingface_hub import snapshot_download |
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path = snapshot_download(repo_id="kycai23/c3cf") |
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``` |
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## Training & Evaluation |
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Refer to the [research article]() mentioned above. |
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