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---
title: Submission Template
emoji: 🔥
colorFrom: yellow
colorTo: green
sdk: docker
pinned: false
---
# Random Forest Model for Climate Disinformation Classification
## Model Description
This is a random forest model for the Frugal AI Challenge 2024, specifically for the audio classification task of identifying illegal deforestation.
### Intended Use
- **Primary intended uses**: Illegal deforestation classification model
- **Primary intended users**: Researchers and developers participating in the Frugal AI Challenge
- **Out-of-scope use cases**: Not intended for production use or real-world classification tasks
## Training Data
The model uses the rfcx/frugalai dataset:
- Size: ~50000 examples
- Split: 80% train, 20% test
- 2 categories of audio category
### Labels
0. chainsaw (positively identifying a chainsaw)
1. environment (not containing a chainsaw).
## Performance
### Metrics
- **Accuracy**: ~89.3%
- **Environmental Impact**:
- Emissions tracked in gCO2eq
- Energy consumption tracked in Wh
### Model Architecture
The model implements a random forest model used on pre-processed data. The pre-processing consists in a resampling, a Fourier decomposition and a standard scaler.
## Environmental Impact
Environmental impact is tracked using CodeCarbon, measuring:
- Carbon emissions during inference
- Energy consumption during inference
This tracking helps establish a baseline for the environmental impact of model deployment and inference.
## Limitations
- Takes some time to do the pre-processing.
## Ethical Considerations
- Environmental impact is tracked to promote awareness of AI's carbon footprint
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