Flawless Classification Model Concerns

Requirements Clarification & Assessment

1. Understanding "Flawless":

   • Clarify what is meant by "flawless". Is it 100% accuracy on training data, validation data, or real-world data?
   • Determine the scope and domain of the customer behavior being predicted.

2. Data Quality and Sources:

   • Assess the quality, volume, and diversity of the data used for model training.
   • Identify the sources of data and ensure they are representative of real-world scenarios.

3. Model Objectives:

   • Define the primary objectives of the model. Is it to maximize accuracy, interpretability, or adaptability?
   • Consider stakeholder requirements and expectations from the model.

4. Ethical and Legal Considerations:

   • Identify any ethical concerns related to the use of the model, such as data privacy and bias.
   • Ensure compliance with relevant regulations, such as GDPR or CCPA.

5. Technical Constraints:

   • Evaluate the computational resources available for model training and deployment.
   • Determine the infrastructure requirements for integrating the model into existing systems.