---
license: mit
library_name: sklearn
tags:
  - sklearn
  - random-forest
  - iris
  - classification
---

# Iris Classifier

A simple Random Forest classifier trained on the classic Iris dataset.

## Model details
- **Algorithm**: Random Forest (100 trees)
- **Library**: scikit-learn
- **Dataset**: Iris (150 samples, 4 features, 3 classes)
- **Test accuracy**: 1.00

## Input
4 numeric features:
1. Sepal length (cm)
2. Sepal width (cm)
3. Petal length (cm)
4. Petal width (cm)

## Output
Predicted species class: `0` (setosa), `1` (versicolor), or `2` (virginica)

## Usage
```python
import skops.io as sio
from huggingface_hub import hf_hub_download

path = hf_hub_download(repo_id="mosesalphonse/iris-classifier", filename="model.skops")
model = sio.load(path, trusted=True)

prediction = model.predict([[5.1, 3.5, 1.4, 0.2]])
print(prediction)
```