What type of analysis would you conduct to determine if your model is performing well?

Diagnostic analysis focuses on understanding and evaluating the performance of a model by identifying the factors that contribute to its successes or failures. This type of analysis allows for a detailed examination of the model's predictions compared to actual outcomes, helping to uncover patterns, correlations, or anomalies that may indicate strengths or weaknesses in the model's design or data inputs.

By conducting diagnostic analysis, you can assess metrics such as accuracy, precision, recall, and F1 score, which provide insights into how well the model is making predictions. Additionally, this analysis can help identify if certain features or data inputs are leading to poor predictions, enabling enhancements or adjustments to improve overall model performance.

While the other types of analysis listed serve different purposes, they do not directly evaluate the model's performance relative to the expected outcomes in the same way that diagnostic analysis does. Descriptive analysis summarizes historical data, predictive analysis forecasts future events based on trends, and prescriptive analysis recommends actions based on predicted outcomes. Thus, for assessing model performance specifically, diagnostic analysis is the most relevant approach.