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README.md

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+---
+library_name: scikit-learn
+tags:
+- scikit-learn
+- joblib
+- iris
+- random-forest
+- classification
+---
+
+# Iris Random Forest Model
+
+Modelo de ejemplo entrenado con el dataset Iris de `scikit-learn` y guardado como `joblib`.
+
+## Variables de entrada
+- sepal length (cm)
+- sepal width (cm)
+- petal length (cm)
+- petal width (cm)
+
+## Clases de salida
+- setosa
+- versicolor
+- virginica
+
+## Métrica principal
+- Accuracy: 0.9667
+
+## Uso rápido
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+
+model_path = hf_hub_download(repo_id="AriasMo/iris-rf-joblib-demo", filename="model.joblib")
+model = joblib.load(model_path)
+```
+
+## Space relacionada
+La interfaz del modelo estará disponible en:
+`https://huggingface.co/spaces/AriasMo/iris-rf-gradio-space`

classification_report.txt

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+              precision    recall  f1-score   support
+
+      setosa       1.00      1.00      1.00        10
+  versicolor       1.00      0.90      0.95        10
+   virginica       0.91      1.00      0.95        10
+
+    accuracy                           0.97        30
+   macro avg       0.97      0.97      0.97        30
+weighted avg       0.97      0.97      0.97        30

metrics.json

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+{
+  "accuracy": 0.9666666666666667,
+  "train_size": 120,
+  "test_size": 30,
+  "features": [
+    "sepal length (cm)",
+    "sepal width (cm)",
+    "petal length (cm)",
+    "petal width (cm)"
+  ],
+  "classes": [
+    "setosa",
+    "versicolor",
+    "virginica"
+  ],
+  "model_type": "RandomForestClassifier",
+  "random_state": 42
+}

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bcff64eda03aeccda52d4874dd8ef332e23764182a415ec819c0418e24717852
+size 296874

requirements.txt

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+scikit-learn
+joblib
+pandas
+matplotlib
+huggingface_hub