File size: 1,542 Bytes
32fa17a 2814f30 1936bdd 32fa17a 2814f30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
library_name: sklearn
tags:
- regression
- scikit-learn
- UCS
- cement
license: mit
pipeline_tag: tabular-regression
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
---
# RandomForestRegressor for UCS Prediction
## Model Overview
This model is a `RandomForestRegressor` trained to predict the Unconfined Compressive Strength (UCS) of soil-cement mixtures based on the following features:
- **Curing Period (`curing_period`)**: Duration in days.
- **Compaction Rate (`compaction_rate`)**: Numerical value.
- **Cement Percentage (`cement_percent`)**: Percentage of cement in the mixture.
## Performance
The model achieved an R² score of **0.968** during cross-validation, indicating a high level of accuracy in predicting UCS values.
## Feature Ranges
- **Curing Period**: Min = 0.0 days, Max = 28.0 days, Mean = 11.06 days
- **Compaction Rate**: Min = 0.5, Max = 1.25, Mean = 0.989
- **Cement Percentage**: Min = 0.0%, Max = 10.0%, Mean = 5.77%
## Usage
To utilize this model, load the `model.joblib` file and input data within the specified feature ranges to obtain UCS predictions.
## Limitations
The model is calibrated for predictions within the specified feature ranges. Using it outside these ranges may result in less accurate predictions. It is specifically designed for soil-cement mixtures and may not be applicable to other materials.
## Author
Bogdan TEODORU
Gheorghe Asachi Technical University of Iasi (TUIASI)
## Contact
For inquiries or suggestions, please contact bteodoru@tuiasi.ro. |