Welcome to the Binary Classifier Models repository. This library provides two simple, easy-to-understand implementations of fundamental machine learning algorithms for binary classification tasks, built with pure Python and NumPy.

• Decision Tree (dt.py): A depth-limited decision tree that splits features based on thresholding at 0.5.
• Perceptron (main.py): A classic single-layer perceptron suitable for linearly separable data.

1. Clone the repository:

   git clone https://github.com/myrodar/AI_cookbook.git
   cd your-repo

2. (Optional) Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

Coming soon!

• DT(opts: dict)
  Initialize with options:

  • opts['maxDepth']: Maximum depth of the tree.

• train(X: np.ndarray, Y: Sequence[int])
  Build the decision tree from the data.

• predict(x: np.ndarray) -> int
  Predict the label (-1 or 1) for a single sample.

• __repr__() / displayTree()
  Print a human-readable representation of the tree.

This folder contains two scripts.

This folder contains two scripts:

• main.py is the main script for the raw perceptron. It contains the Perceptron class and its main function to instantiate, train, and predict based on the data you provide.

• test.py uses the Perceptron implementation from main.py and generates a visual representation of the decision boundary, allowing you to see how the single neuron learns.

• Challenge: Try swapping the generated dataset for the Iris dataset. Be sure to use only two classes from the Iris dataset, as a perceptron can only perform binary classification.

Contributions are welcome. To contribute: Contributions are welcome. To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/your-feature.
3. Commit your changes and push your branch.
4. Open a pull request.

This project is licensed under the MIT License. See the LICENSE file for details.