Bias and Variance in Models

Requirements Clarification & Assessment

1. Understanding Bias and Variance

   • Bias: Represents the error due to overly simplistic assumptions in the learning algorithm. High bias can cause an algorithm to miss relevant relations between features and target outputs (underfitting).
   • Variance: Represents the error due to excessive sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data (overfitting).

2. Interview Question Context

   • The question requires an understanding of how a model can simultaneously exhibit high bias and high variance, which is less common but possible under certain conditions.
   • The answer should provide a concrete example of a model displaying both issues and suggest potential solutions to mitigate them.

3. Objective

   • Clearly illustrate the concepts of high bias and high variance using a specific machine learning model example.
   • Provide a detailed explanation of how the model's structure can lead to both high bias and high variance.
   • Discuss strategies to address these issues in the model.