Recall & Review
beginner
What is bias in machine learning?
Bias is the error from wrong assumptions in the learning algorithm. It causes the model to miss relevant relations between features and target outputs, leading to underfitting.
Click to reveal answer
beginner
What does variance mean in the context of machine learning models?
Variance is the error from sensitivity to small fluctuations in the training set. High variance means the model learns noise as if it were true patterns, causing overfitting.
Click to reveal answer
beginner
Explain the bias-variance tradeoff in simple terms.
The bias-variance tradeoff is balancing two types of errors: too simple models have high bias and miss patterns, too complex models have high variance and learn noise. The goal is to find a model that balances both for best predictions.
Click to reveal answer
intermediate
How does increasing model complexity affect bias and variance?
Increasing model complexity usually decreases bias because the model can fit the training data better, but it increases variance because the model may fit noise and not generalize well.
Click to reveal answer
intermediate
What is a practical way to detect if a model is suffering from high bias or high variance?
Check training and validation errors: high training and validation errors suggest high bias (underfitting), while low training error but high validation error suggests high variance (overfitting).
Click to reveal answer
What does high bias usually cause in a machine learning model?
Which of the following is a sign of high variance?
What happens to bias and variance when you increase model complexity?
Why is the bias-variance tradeoff important?
Which error type is caused by a model that is too simple?
Describe the bias-variance tradeoff and why it matters in machine learning.
How can you tell if a model is overfitting or underfitting by looking at training and validation errors?