In the context of Polynomial Regression, using too low a degree may lead to _________, while too high a degree may lead to _________.
- accuracy, inaccuracy
- overfitting, underfitting
- stability, instability
- underfitting, overfitting
Using too low a degree may cause the model to be too simple and underfit the data, while too high a degree can lead to a complex model that overfits the data.
Loading...
Related Quiz
- A colleague has built a Polynomial Regression model and suspects overfitting. What diagnostic tools and techniques would you recommend to confirm or deny this suspicion?
- Regularization techniques help in preventing overfitting. Which of these is NOT a regularization technique: Batch Normalization, Dropout, Adam Optimizer, L1 Regularization?
- You are dealing with a dataset having many irrelevant features. How would you apply Lasso regression to deal with this scenario?
- What is the primary goal of the K-Means Clustering algorithm?
- Given a scenario where computational resources are limited, but there's a need to process high-resolution images for feature detection, what approach might be taken in the design of the neural network to balance performance and computational efficiency?