| | --- |
| | 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* |
| |
|
