What is the effect of increasing the regularization parameter in Ridge and Lasso regression?
- Decrease in bias and increase in variance
- Increase in bias and decrease in variance
- Increase in both bias and variance
- No change in bias and variance
Increasing the regularization parameter leads to greater regularization strength, resulting in an increase in bias and a decrease in variance, thus constraining the model complexity.
Loading...
Related Quiz
- How does reinforcement learning contribute to the development of smart energy management systems?
- Explain how the coefficients of Simple Linear Regression can be interpreted in terms of correlation.
- The ________ component in PCA explains the highest amount of variance within the data.
- Can you explain the concept of Semi-Supervised Learning and how it bridges the gap between supervised and unsupervised learning?
- __________ learning utilizes both labeled and unlabeled data, often leveraging the strengths of both supervised and unsupervised learning.