In a case where both overfitting and underfitting are concerns depending on the chosen algorithm, how would you systematically approach model selection and tuning?
- Increase model complexity
- Reduce model complexity
- Use L1 regularization
- Use grid search with cross-validation
Systematic approach involves the use of techniques like grid search with cross-validation to explore different hyperparameters and model complexities. This ensures that the selected model neither overfits nor underfits the data and generalizes well to unseen data.
Loading...
Related Quiz
- In K-Means clustering, the algorithm iteratively assigns each data point to the nearest _______, recalculating the centroids until convergence.
- You are asked to include an interaction effect between two variables in a Multiple Linear Regression model. How would you approach this task, and what considerations would you need to keep in mind?
- In what scenarios might a custom distance metric be needed in KNN, and how would you go about implementing it?
- ____________ Learning, a subset of Machine Learning, is essential in training robots to perform specific tasks in manufacturing industries.
- How can you determine the degree of the polynomial in Polynomial Regression?