In a situation where the features in your dataset are at very different scales, which regularization technique would you choose and why?
- L1 Regularization because of complexity
- L1 Regularization because of sparsity
- L2 Regularization because of scalability
- L2 Regularization because of sensitivity to noise
L2 Regularization (Ridge) would be chosen when features are at different scales because it scales the coefficients without completely eliminating them, preserving information. It can prevent overfitting while considering all features.
Loading...
Related Quiz
- What is the F1-Score, and why might you use it instead of Precision and Recall?
- Describe how Machine Learning algorithms are implemented in sentiment analysis and customer feedback systems.
- Machine learning algorithms trained on medical images to detect anomalies are commonly referred to as ________.
- In healthcare, Machine Learning can help in early detection of ____________ and ____________.
- When using K-means clustering, why is it sometimes recommended to run the algorithm multiple times with different initializations?