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Data Interview Question

Data Anomalies on Model Integrity

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Requirements Clarification & Assessment

  1. Understanding the Problem:

    • The logistic regression model relies heavily on a specific variable.
    • Due to a data quality issue, some values in this variable have lost their decimal points, turning values like 100.00 into 10000.
  2. Assessing the Impact:

    • Determine if the altered values are outliers or influential points.
    • Understand the role of the variable in the model and how its distortion affects model integrity.
  3. Defining the Goal:

    • Ensure the model remains valid and accurate.
    • Identify and correct the data quality issue without compromising model performance.
  4. Constraints & Assumptions:

    • Assume the variable's original value range is known.
    • Assume the data quality issue is limited to decimal point loss.
    • Assume access to tools for detecting influential points (e.g., Cook's distance).