Create README.md

README.md

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+license: cc0-1.0
+---
+
+# Model Card for Iris Decision Tree Model (iris_dt.joblib)
+
+## Model Summary
+
+The `iris_dt.joblib` file contains a pre-trained Decision Tree model for classifying iris flowers into three species (*Iris setosa*, *Iris versicolor*, and *Iris virginica*) based on their features: sepal length, sepal width, petal length, and petal width. This model was trained using the classic Iris dataset and is intended for educational purposes, benchmarking, and experimentation with decision tree algorithms.
+
+## Supported Tasks and Leaderboards
+
+The model supports the following task:
+- **Tabular Classification**: Predict the species of an iris flower given its numerical features.
+
+No active leaderboard is associated with this model.
+
+## Languages
+
+The model does not process textual data; it exclusively handles numerical features.
+
+## Model Structure
+
+### Input Features
+
+The model expects the following input features:
+- Sepal length (float)
+- Sepal width (float)
+- Petal length (float)
+- Petal width (float)
+
+### Output
+
+The model outputs a categorical prediction representing the flower species:
+- 0 = *Iris setosa*
+- 1 = *Iris versicolor*
+- 2 = *Iris virginica*
+
+### Training Data
+
+This model was trained on the Iris dataset, which consists of 150 samples evenly distributed across the three classes. The dataset is publicly available and widely used as a benchmark in machine learning.
+
+## Considerations for Using the Model
+
+### Intended Use Cases
+
+The model is suitable for:
+- Educational purposes: Understanding decision tree algorithms.
+- Benchmarking: Comparing performance against other models.
+- Experimentation: Testing feature importance and decision boundaries.
+
+### Limitations
+
+The model was trained on a small dataset (150 samples) and may not generalize well to real-world applications. Additionally, decision trees are prone to overfitting when applied to small datasets.
+
+### Ethical Considerations
+
+The Iris dataset does not contain sensitive or personal information. However, users should ensure that this model is not applied to tasks beyond its intended scope.
+
+## Additional Information
+
+### Homepage
+
+Not applicable.
+
+### Repository
+
+The Iris dataset and decision tree training example are available through [scikit-learn](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html).
+
+### Citation
+
+If you use this model, please cite the original dataset paper: