To establish a foundation for our email spam detection project, it is crucial to develop a baseline model that serves as a starting point for subsequent model iterations and improvements. This GitHub issue aims to address the development and evaluation of a baseline model for email spam detection.
Goals:
Model Selection: Identify an appropriate machine learning algorithm or model architecture as the baseline for our email spam detection system.
Feature Engineering: Perform essential feature engineering steps to transform the preprocessed email data into a suitable format for the selected baseline model.
Model Development: Implement and train the chosen baseline model using the preprocessed dataset. Utilize established machine learning libraries or frameworks to facilitate model development and ensure reproducibility.
Evaluation Metrics: Define appropriate evaluation metrics to assess the performance of the baseline model. Common metrics for spam detection include accuracy, precision, recall, F1 score, and receiver operating characteristic (ROC) curves.
Model Evaluation: Evaluate the performance of the baseline model using appropriate evaluation metrics and cross-validation techniques. Analyze the model's strengths, weaknesses, and limitations based on the obtained results.
Documentation: Document the development process, including the chosen model, feature engineering techniques, hyperparameter settings, and evaluation metrics. This documentation will serve as a reference for future model iterations and comparisons.
Tasks:
Select an appropriate machine learning algorithm or model architecture as the baseline for email spam detection.
Perform feature engineering to prepare the preprocessed dataset for model training.
Implement and train the baseline model using established machine learning libraries or frameworks.
Define evaluation metrics suitable for assessing the performance of the baseline model.
Evaluate the model's performance using appropriate cross-validation techniques.
Document the baseline model development process and results for future reference.
Expected Outcome:
By developing a baseline model for email spam detection, we aim to establish a starting point for the project and provide a benchmark for subsequent model iterations. This will facilitate the evaluation of future model enhancements and improvements, leading to more accurate and effective spam detection.
dfrancis-tech
commented
1 year ago
To establish a foundation for our email spam detection project, it is crucial to develop a baseline model that serves as a starting point for subsequent model iterations and improvements. This GitHub issue aims to address the development and evaluation of a baseline model for email spam detection.
Goals:
Tasks:
Expected Outcome:
By developing a baseline model for email spam detection, we aim to establish a starting point for the project and provide a benchmark for subsequent model iterations. This will facilitate the evaluation of future model enhancements and improvements, leading to more accurate and effective spam detection.