Create README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: tabular-classification
+tags:
+- sklearn
+- classification
+- iris
+- tabular
+datasets:
+- brjapon/iris
+metrics:
+- accuracy
+library_name: scikit-learn
+new_version: "v1.0"
+model-index:
+- name: Iris Decision Tree
+  results:
+  - task:
+      type: tabular-classification
+      name: Classification
+    metrics:
+    - type: accuracy
+      value: 0.97
+      name: Test Accuracy
+---
+
+# Iris Classification Models
+
+This repository starts with a **Decision Tree** model trained on the classic **Iris dataset**. The model classifies iris flowers into three species—*setosa*, *versicolor*, or *virginica*—based on four numeric features (sepal length, sepal width, petal length, and petal width).  
+
+Because of its small size and simplicity, this model is intended primarily for **demonstration and educational** purposes.
+
+## Model Description
+- **Framework**: [Scikit-Learn](https://scikit-learn.org/stable/)  
+- **Algorithm**: Decision Tree (`DecisionTreeClassifier` class)  
+- **Hyperparameters**:  
+  - Defaults for Decision Tree in Scikit-Learn
+
+### Intended Uses
+- **Education/Proof-of-Concept**: Demonstrates loading a scikit-learn model from the Hugging Face Hub.  
+- **Beginner ML Tutorials**: Introduction to classification tasks, usage of Hugging Face model hosting, and deploying simple demos in Spaces.
+
+### Limitations
+- **Dataset Size**: The Iris dataset is small (150 samples). Performance metrics may not extrapolate to real-world scenarios.  
+- **Domain Constraints**: The dataset only covers three iris species and may not generalize to other types of flowers.  
+- **Not Production-Ready**: This model is not suited for critical applications (e.g., healthcare, autonomous vehicles).  
+
+## How to Use
+To use this model, you can load the `.joblib` file from the Hub in Python code:
+
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Accompanying dataset is hosted in Hugging Face under 'Jesus02/iris-clase'
+model_path = hf_hub_download(repo_id="brjapon/iris",
+                             filename="iris_dt.joblib",
+                             repo_type="model")
+
+model = joblib.load(model_path)
+
+# Example prediction (random values below)
+sample_input = [[5.1, 3.5, 1.4, 0.2]]
+prediction = model.predict(sample_input)
+print(prediction)  # e.g., [0] which might correspond to 'setosa'
+```
+
+## Training Procedure
+- **Training Data**: 80% of the 150-sample Iris dataset (120 samples).  
+- **Validation Data**: 20% (30 samples).  
+- **Steps**:  
+  1. Loaded dataset (obtained from HF repository `brjapon/iris`) 
+  2. Split into training and test sets with `train_test_split`
+  3. Trained Decision Tree model with default settings
+  4. Evaluated accuracy on the test set
+
+## Performance
+Using a random 80/20 split, the model typically achieves **~97%** accuracy on the test subset. Actual results may vary depending on your specific train/test split random state.
+
+## Limitations & Bias
+- The Iris dataset is not representative of modern, large-scale classification tasks.  
+- Results should not be generalized beyond the included species and scenario.