**Question:**Which of the following are ways to regularize a model?**Options:**- [ ] regularization- [ ] using a more complex model- [ ] in polynomial regression reducing the number of degrees- [ ] adding more features- [ ] using a simpler model**Explanation:**This question explores the different techniques employed to regularize a model, focusing on methods that prevent overfitting and improve model generalization. **Question:** A model is likely to be **underfitting** if it has ____.**Options:** - ○ low bias - ○ high bias - ○ high variance - ○ low variance **Explanation:** This question assesses your understanding of the concept of underfitting in machine learning models. Underfitting occurs when a model is too simple to capture the underlying patterns in the data. Answering this question correctly requires knowing the relationship between bias, variance, and underfitting.

“Since you have posted multiple questions, we will provide the solution only to the first question as per our Q&A guidelines. Please repost the remaining questions separately.”Regularization is a technique used in machine learning to prevent overfitting and improve the generalization of a model. In this step, we will briefly introduce the concept of regularization without providing the actual answer or explanation.Out of the given options, the correct ways to regularize a model are:- Regularization: It involves adding a penalty term to the loss function, such as L1 or L2 regularization, to discourage large weights in the model.- Reducing the number of degrees in polynomial regression: By reducing the number of degrees in polynomial regression, the model becomes less complex and less prone to overfitting.- Using a simpler model: Choosing a simpler model architecture, such as reducing the number of layers or nodes in a neural network, can help regularize the model.The incorrect ways to regularize a model are:- Using a more complex model: Using a more complex model tends to increase the risk of overfitting, rather than regularizing the model.- Adding more features: Adding more features can potentially increase the model's complexity and lead to overfitting, instead of regularization.The correct ways to regularize a model are regularization, reducing the number of degrees in polynomial regression, and using a simpler model.

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Which of the following are ways to regularize a model? □ regularization □ using a more complex model in polynomial regression reducing the number of degrees adding more features using a simpler model

Which of the following are ways to regularize a model? □ regularization □ using a more complex model in polynomial regression reducing the number of degrees adding more features using a simpler model

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

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