To improve the performance of our email spam detection system, it is essential to compare and analyze the performance of various machine learning algorithms. This GitHub issue aims to address the need for comparing and evaluating different algorithms against the baseline model to identify the most effective approach for detecting spam emails.
Goals:
Algorithm Selection: Choose a set of machine learning algorithms suitable for email spam detection. This may include decision trees, logistic regression, support vector machines (SVM), random forests, gradient boosting, or deep learning models. Consider both traditional and advanced techniques to cover a broad range of possibilities.
Preprocessing Consistency: Ensure consistent preprocessing of the dataset across all algorithms to maintain fairness during the comparison. Apply the same preprocessing steps used for the baseline model to maintain consistency in feature engineering and data preparation.
Model Implementation: Implement and train each selected machine learning algorithm using the preprocessed dataset. Utilize established machine learning libraries or frameworks to facilitate model development and training. Ensure reproducibility and transparency in the implementation process.
Performance Evaluation: Evaluate the performance of each algorithm using appropriate evaluation metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. Utilize techniques like cross-validation to obtain reliable and robust performance estimates.
Statistical Analysis: Perform statistical analysis to compare the performance of the different algorithms against the baseline model. This may involve hypothesis testing, confidence intervals, or other statistical techniques to determine if there is a significant difference in performance. Analyze and interpret the results to identify the most promising algorithms.
Documentation and Reporting: Document the implementation details, evaluation results, and statistical analysis in a clear and organized manner. Provide insights and observations regarding the performance differences among the algorithms. Generate visualizations, tables, or summary reports to facilitate understanding and decision-making.
Tasks:
Select a set of machine learning algorithms suitable for email spam detection.
Ensure consistent preprocessing of the dataset across all algorithms.
Implement and train each selected algorithm using the preprocessed dataset.
Evaluate the performance of each algorithm using appropriate evaluation metrics and cross-validation techniques.
Perform statistical analysis to compare the performance of the algorithms against the baseline model.
Document the implementation details, evaluation results, and statistical analysis.
Expected Outcome:
By comparing and analyzing the performance of different machine learning algorithms, we aim to identify the most effective approaches for email spam detection. This analysis will provide valuable insights for selecting the algorithms with the highest performance potential, guiding future development and optimization efforts to enhance our spam detection system.
dfrancis-tech
commented
1 year ago
To improve the performance of our email spam detection system, it is essential to compare and analyze the performance of various machine learning algorithms. This GitHub issue aims to address the need for comparing and evaluating different algorithms against the baseline model to identify the most effective approach for detecting spam emails.
Goals:
Tasks:
Expected Outcome:
By comparing and analyzing the performance of different machine learning algorithms, we aim to identify the most effective approaches for email spam detection. This analysis will provide valuable insights for selecting the algorithms with the highest performance potential, guiding future development and optimization efforts to enhance our spam detection system.