Data Visualization and Model Comparison

[karsterr]

This issue focuses on the implementation of data visualization techniques to analyze and compare the performance of the machine learning algorithms recently added to our project. Visualizations will help us gain insights into the strengths and weaknesses of each model and provide valuable information for model selection and improvement.

[karsterr]

Tasks:

Data Preparation: Ensure that the necessary datasets are prepared for model evaluation and comparison. This may involve preprocessing steps such as data cleaning, feature scaling, and splitting into training and testing sets.

Visualization Design: Design visualizations that effectively showcase the performance metrics and characteristics of each model. This may include confusion matrices, ROC curves, precision-recall curves, learning curves, and feature importance plots.

Model Comparison: Implement a systematic comparison framework to evaluate the performance of the different machine learning algorithms. This could involve metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC).

Interactive Visualization: Consider implementing interactive visualizations using tools like Plotly or Bokeh to allow users to explore the data and model results dynamically.

Documentation and Reporting: Document the visualizations and comparison results comprehensively, explaining the interpretation of each visualization and providing insights into the strengths and weaknesses of the models.

[karsterr]

Expected Outcome: By the end of this issue, we should have a set of informative visualizations and a detailed comparison report that will guide us in selecting the most suitable machine learning algorithm for our project's objectives.