Bias Variance Tradeoff

The Bias-Variance Tradeoff is a fundamental concept in machine learning and statistics that describes the relationship between a model’s ability to generalize and its complexity. The goal is to minimize the total prediction error, which can be expressed as:

Total Error=Bias^2 + Variance + Irreducible Error

1. Bias:
   Bias measures the error introduced by approximating a real-world problem (which may be very complex) using a simplified model.
   • High Bias: The model makes strong assumptions and fails to capture the underlying patterns – known as underfitting.
   • Low Bias: The model closely represents the real relationship between input and output.
   • Example: Using a linear regression model to predict house prices when the relationship between size and price is nonlinear.
2. Variance:
   Variance measures how much the model’s predictions would change if it were trained on a different dataset.
   • High Variance: The model learns even small fluctuations or noise in the data – known as overfitting.
   • Low Variance: The model’s predictions remain stable across different data samples.
   • Example: A deep neural network perfectly fitting training data but performing poorly on test data.

Real-Life Analogy:
Imagine preparing for an exam:

• If you study only the basics (high bias), you can’t answer detailed questions.
• If you memorize every question (high variance), you get confused with new ones.
• The best student balances both – understanding the core concepts and adapting to new problems.