Preventing Overfitting in Tree Models

Requirements Clarification & Assessment

1. Understanding the Problem Context:

   • Goal of the ML Model: Determine whether the task is binary or multi-class classification.
   • Dataset Characteristics: Assess the size, quality, and features of the dataset. Identify any class imbalance issues.
   • Feature Importance: Evaluate whether feature engineering has been conducted and how feature importance is determined.

2. Data Preparation:

   • Data Sufficiency: Ensure there's enough data to train the model effectively without overfitting.
   • Feature Engineering: Check if relevant features have been engineered and if unnecessary features have been removed.

3. Model Requirements:

   • Hyperparameter Tuning: Identify the hyperparameters that need tuning to prevent overfitting.
   • Evaluation Metrics: Decide on appropriate evaluation metrics to assess the model's performance, especially in the presence of class imbalance.

4. Clarifying Questions:

   • What tree-based model is being used (e.g., single decision tree, Random Forest, Gradient Boosting)?
   • How is the model's performance currently being evaluated?
   • Are there any constraints on computational resources or model complexity?