| | --- |
| | language: en |
| | license: mit |
| | tags: |
| | - sklearn |
| | - iris |
| | - classification |
| | - random-forest |
| | --- |
| | |
| | # 🌸 Iris Flower Classifier |
| |
|
| | A simple Random Forest classifier trained on the classic Iris dataset. |
| |
|
| | ## Model Details |
| |
|
| | | Property | Value | |
| | |----------------|--------------------------| |
| | | Algorithm | Random Forest | |
| | | n_estimators | 100 | |
| | | Test Accuracy | 0.9000 | |
| | | Train samples | 120 | |
| | | Test samples | 30 | |
| | |
| | ## Classes |
| | |
| | The model predicts one of three Iris species: |
| | - `setosa` |
| | - `versicolor` |
| | - `virginica` |
| | |
| | ## Usage |
| | |
| | ```python |
| | import pickle, numpy as np |
| | |
| | with open("model.pkl", "rb") as f: model = pickle.load(f) |
| | with open("scaler.pkl", "rb") as f: scaler = pickle.load(f) |
| | |
| | # sepal length, sepal width, petal length, petal width (all in cm) |
| | X = np.array([[5.1, 3.5, 1.4, 0.2]]) |
| | X_scaled = scaler.transform(X) |
| | prediction = model.predict(X_scaled) |
| | print(prediction) # e.g. [0] → setosa |
| | ``` |
| | |
| | ## Per-class Metrics |
| | |
| | | Class | Precision | Recall | F1-score | |
| | |-------------|-----------|--------|----------| |
| | | setosa | 1.0000 | 1.0000 | 1.0000 | |
| | | versicolor | 0.8182 | 0.9000 | 0.8571 | |
| | | virginica | 0.8889 | 0.8000 | 0.8421 | |
| | |
