Assessing Decision Tree Model Effectiveness

Requirements Clarification & Assessment

1. Objective Understanding:

   • The primary objective is to predict whether a client will repay a personal loan using a decision tree model. This involves a binary classification problem (repay vs. not repay).

2. Data Nature:

   • Evaluate the dataset to understand the types of features available (numerical/categorical).
   • Identify missing values and decide on imputation strategies.
   • Ensure the dataset includes relevant features such as credit history, income, debt-to-income ratio, etc.

3. Problem Complexity:

   • Determine if the problem involves complex relationships or interactions between variables that might not be well-captured by a simple decision tree.

4. Interpretability Needs:

   • Decision trees are highly interpretable, which is beneficial in a financial context where understanding the decision-making process is crucial for compliance and stakeholder trust.

5. Regulatory Compliance:

   • Ensure the model adheres to financial regulations and ethical considerations, including fairness across demographic groups.

6. Performance Metrics:

   • Identify critical performance metrics such as precision, recall, F1-score, and AUC-ROC, focusing on minimizing false positives and false negatives due to their financial implications.

7. Existing Solutions:

   • Review any existing models or approaches used within the institution for similar tasks to benchmark the decision tree model against.