metadata

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

chainsaw (positively identifying a chainsaw)
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