Random Forests

Requirements Clarification & Assessment

• Objective Understanding: The primary goal is to optimize a Random Forest model for improved performance metrics such as accuracy, precision, recall, or other relevant metrics depending on the task (classification or regression).
• Data Characteristics: Understand the dataset characteristics, including the number of features, instances, and the nature of the data (balanced or imbalanced).
• Evaluation Metrics: Define clear metrics for evaluation. For classification, this might include accuracy, F1 score, precision, recall, or AUC. For regression, consider RMSE or MAE.
• Constraints: Consider computational constraints such as time and resources available for model training and tuning.
• Environment: Confirm the tools and libraries available for implementation (e.g., Scikit-learn, TensorFlow, etc.).
• Project Timeline: Establish a timeline for model development, including time for hyperparameter tuning and validation.

Stakeholder Input

• Gather input from data stakeholders to ensure alignment on goals and evaluation criteria.
• Confirm any domain-specific considerations that may affect model requirements or performance criteria.