Bias and Variance in Machine Learning

Requirements Clarification & Assessment

1. Understanding the Concepts:

   • Bias: Refers to 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: Refers to 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 rather than the intended outputs (overfitting).

2. Identifying the Problem:

   • Determine if the model is underfitting or overfitting based on its performance on training and test datasets.
   • Assess the complexity of the model in relation to the amount of data available.

3. Defining Goals:

   • Achieve a balance between bias and variance to ensure the model generalizes well to unseen data.
   • Optimize model performance by minimizing both training and test errors.

4. Constraints and Limitations:

   • Limited computational resources may restrict the complexity of models that can be evaluated.
   • Data quality and quantity can impose constraints on how well the bias-variance balance can be managed.