The addition of _________ in the loss function is a common technique to regularize the model and prevent overfitting.
- bias
- learning rate
- regularization terms
- weights
Regularization terms (like L1 or L2 penalties) in the loss function constrain the model, reducing the risk of overfitting by preventing large weights.
Loading...
Related Quiz
- You are using Bootstrapping to estimate the confidence interval for a model parameter. Explain how the process works.
- Hierarchical clustering can be broadly classified into two types based on how the hierarchy is constructed. What are these two types?
- When regular Q-learning takes too much time to converge in a high-dimensional state space (e.g., autonomous vehicle parking), what modification could help it learn faster?
- When determining the number of clusters (K) for K-means clustering, which method involves plotting the variance as K increases and looking for an "elbow" in the plot?
- An educational institution wants to personalize its online learning platform for individual student needs. How would you leverage Machine Learning to achieve this goal?