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README.md

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+---
+license: mit
+tags:
+  - sklearn
+  - classification
+  - iris
+  - random-forest
+  - tabular
+library_name: sklearn
+---
+
+# 🌸 Iris Classifier — Random Forest
+
+A simple **Random Forest** classifier trained on the classic
+[Iris dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html).
+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
+
+```python
+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*