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Data Interview Question

Preventing Overfitting in Tree Models

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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?