How do you explain the distinction between bias and variance in machine learning?

Question Analysis

This question tests your understanding of a fundamental concept in machine learning: the bias-variance tradeoff. Bias and variance are two types of errors that can affect the performance of a machine learning model. Understanding these concepts is crucial for diagnosing and improving model performance. The interviewer is looking for a clear explanation that distinguishes between bias and variance and how they relate to model complexity and error.

Answer

In machine learning, bias and variance are two key sources of error that affect the performance of a model:

• Bias refers to the error introduced by approximating a real-world problem, which may be complex, by a simplified model. High bias can cause an algorithm to miss the relevant relations between features and target outputs, leading to underfitting. Models with high bias pay very little attention to the training data and oversimplify the model.

• Variance refers to the error introduced by sensitivity to small fluctuations in the training dataset. High variance can cause an algorithm to model the random noise in the training data, leading to overfitting. Models with high variance pay too much attention to the training data and learn from the noise as if it were a true signal.

The bias-variance tradeoff is the balance between these two types of error:

• With a high-bias/low-variance model, the model is simple with few parameters, leading to underfitting.
• With a low-bias/high-variance model, the model is complex with many parameters, leading to overfitting.

The goal is to find a suitable balance where the combined error (bias + variance) is minimized to improve model performance on unseen data.