Choosing Between Two Models

Requirements Clarification & Assessment

1. Understanding the Problem Context

   • Nature of the Task: Determine whether the task is classification or regression. This affects the choice of metrics.
   • Business Objectives: Identify the primary business goal. Is it purely accuracy-driven, or are there other factors like interpretability and scalability?
   • Data Characteristics: Assess the data distribution. Is it balanced or imbalanced? This can influence the effectiveness of accuracy as a metric.

2. Model Evaluation Metrics

   • Accuracy Limitations: Recognize that accuracy alone might not suffice, especially in imbalanced datasets.
   • Alternative Metrics: Consider precision, recall, F1 score, and AUC-ROC as potential evaluation metrics.

3. Business Impact and Constraints

   • Cost of Errors: Evaluate the cost implications of false positives and false negatives.
   • Interpretability Needs: Determine if the business requires model interpretability over mere accuracy.
   • Scalability Requirements: Assess the scalability of the model for real-time predictions and large datasets.

4. Operational Considerations

   • Training and Testing Time: Consider the time required to train and test the models.
   • Resource Availability: Analyze the computational resources available for model deployment.