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README.md
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---
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license: mit
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library_name: sklearn
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tags:
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- sklearn
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- random-forest
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- iris
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- classification
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---
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# Iris Classifier
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A simple Random Forest classifier trained on the classic Iris dataset.
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## Model details
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- **Algorithm**: Random Forest (100 trees)
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- **Library**: scikit-learn
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- **Dataset**: Iris (150 samples, 4 features, 3 classes)
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- **Test accuracy**: 1.00
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## Input
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4 numeric features:
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1. Sepal length (cm)
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2. Sepal width (cm)
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3. Petal length (cm)
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4. Petal width (cm)
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## Output
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Predicted species class: `0` (setosa), `1` (versicolor), or `2` (virginica)
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## Usage
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```python
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import skops.io as sio
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from huggingface_hub import hf_hub_download
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path = hf_hub_download(repo_id="mosesalphonse/iris-classifier", filename="model.skops")
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model = sio.load(path, trusted=True)
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prediction = model.predict([[5.1, 3.5, 1.4, 0.2]])
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print(prediction)
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```
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