Algorithm Comparison

Requirements Clarification & Assessment

To effectively determine if one algorithm outperforms another, it's crucial to first clarify and assess the requirements and constraints of the problem at hand. This involves understanding:

• Objective: What is the primary goal? Is it maximizing accuracy, minimizing computational cost, ensuring interpretability, or a combination of these?
• Data Characteristics: What is the nature of the data? Consider factors such as size, dimensionality, presence of missing values, and class imbalance.
• Operational Constraints: What are the computational resources available? Are there limitations on memory usage, processing power, or time?
• Business Context: How important is the model's interpretability for stakeholders? Is there a need for real-time predictions?
• Performance Metrics: What metrics are most relevant to the task? For classification, are precision and recall more important than accuracy? For regression, is RMSE more critical than R-squared?

Understanding these requirements is essential for setting the criteria against which algorithms will be evaluated.