Jeroneo/iris-classifier

🌸 Iris Classifier — Random Forest

A simple Random Forest classifier trained on the classic Iris dataset. Deployed automatically via GitHub Actions.

📊 Evaluation Results

Metric	Value
Test Accuracy	0.9333
CV Accuracy (5-fold)	0.9667 ± 0.0211
Train samples	120
Test samples	30

🏗️ Model Details

Parameter	Value
Algorithm	Random Forest
n_estimators	100
max_depth	5

📥 Usage

import pickle, requests, numpy as np
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(repo_id="YOUR_HF_USERNAME/iris-classifier", filename="iris_classifier.pkl")
with open(model_path, "rb") as f:
    model = pickle.load(f)

# Predict  (sepal length, sepal width, petal length, petal width)
sample = np.array([[5.1, 3.5, 1.4, 0.2]])
prediction = model.predict(sample)
class_names = ['setosa', 'versicolor', 'virginica']
print(class_names[prediction[0]])  # -> 'setosa'

📋 Features

The model uses 4 features:

• sepal length (cm)
• sepal width (cm)
• petal length (cm)
• petal width (cm)

🏷️ Classes

setosa, versicolor, virginica

---

Last trained: 2026-03-10