Explain the concept of the bias-variance tradeoff in relation to overfitting and underfitting.
- Both high bias and variance cause overfitting
- Both high bias and variance cause underfitting
- High bias causes overfitting, high variance causes underfitting
- High bias causes underfitting, high variance causes overfitting
High bias leads to underfitting, as the model oversimplifies the data, while high variance leads to overfitting, as the model captures the noise and fluctuations in the training data. Balancing the two is essential for a well-performing model.
Loading...
Related Quiz
- A medical research team is studying the relationship between various health metrics (like blood pressure, cholesterol level) and the likelihood of developing a certain disease. The outcome is binary (disease: yes/no). Which regression model should they employ?
- Balancing the _________ in a training dataset is vital to ensure that the model does not become biased towards one particular outcome.
- __________ learning utilizes both labeled and unlabeled data, often leveraging the strengths of both supervised and unsupervised learning.
- In the context of deep learning, what is the primary use case of autoencoders?
- In what situations would it be appropriate to use Logistic Regression with the Logit link function?