Sure! Below is the code snippet transformed into a GitHub README format:
This repository performs a multiclass classification task using scikit-learn on the Digits dataset. The Digits dataset contains grayscale images of hand-written digits (0 to 9), and the goal is to build and evaluate different machine learning models to accurately predict the digit labels based on the image data.
The Digits dataset is a classic multiclass classification dataset containing grayscale images of hand-written digits (0 to 9). Each sample is an 8x8 image, and there are 10 classes.
The following multiclass classification algorithms are compared in this project:
- Logistic Regression
- k-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Random Forest
- Gaussian Naive Bayes
- Multi-Layer Perceptron (Neural Network)
To assess the performance of each algorithm, the following evaluation metrics are used:
- Accuracy
- Confusion Matrix
- Classification Report (including precision, recall, and F1-score for each class)
- multiclass_classification_digits.ipynb # Jupyter Notebook with the code
- README.md # This README file
To run the multiclass classification and evaluate the models, follow these steps:
- Clone this repository to your local machine.
git clone https://github.com/your-username/multiclass-classification-digits.git- Install the required packages (if not already installed) by running:
pip install scikit-learn matplotlib-
Open the
Digits_Classification_Algorithms.ipynbnotebook and execute the code cells. -
The notebook will train and evaluate various classifiers using the Digits dataset, and the evaluation results will be displayed.
The evaluation results, including accuracy, confusion matrix, and classification report, will be presented for each algorithm. The models' performance on classifying the hand-written digits will be showcased.
Contributions to this project are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or create a pull request.
This project is licensed under the MIT License.
Disclaimer: This project is for educational and research purposes only and does not provide medical advice. Always consult with a medical professional for breast cancer diagnosis and treatment.