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Data Interview Question

Correlated Features on Logistic Regression

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Requirements Clarification & Assessment

Understanding the impact of correlated features on logistic regression involves several key aspects:

  1. Feature Selection Importance:

    • Identify why feature selection is crucial in building robust and interpretable models.
    • Discuss the role of feature selection in enhancing model performance and computational efficiency.
  2. Impact of Correlated Features:

    • Assess how multicollinearity affects logistic regression models.
    • Evaluate the influence of highly correlated features on model coefficients and interpretability.
  3. Confidence Intervals:

    • Determine how multicollinearity affects the confidence intervals of the model coefficients.
    • Understand the implications of wider confidence intervals on statistical inference and model reliability.
  4. Diagnostic Tools:

    • Identify tools and metrics to diagnose multicollinearity, such as Variance Inflation Factor (VIF) and correlation matrices.
  5. Model Assumptions:

    • Review the assumptions of logistic regression related to feature independence and multicollinearity.

Clarifying these requirements will ensure a comprehensive understanding of the problem and guide the development of an effective solution.