**Question:***All of the following are the common strategies to alleviate (i.e., reduce) over-fitting except:*1. Adding regularization terms to the linear regression loss function2. Increasing the number of clusters (K) in the K-means3. Limiting the maximum depth of a decision tree4. Using cross-validation in kNN classification to choose the parameter k**Answer:**Increasing the number of clusters (K) in the K-means*Explanation:*Over-fitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data. Here are some common strategies used to alleviate over-fitting:1. **Adding regularization terms to the linear regression loss function:** This technique helps to prevent over-fitting by penalizing complex models.2. **Limiting the maximum depth of a decision tree:** Controlling the depth of the decision tree helps in reducing its complexity, thus avoiding over-fitting.3. **Using cross-validation in kNN classification to choose the parameter k:** Cross-validation helps in selecting the optimal value of k, thereby balancing bias and variance which in turn reduces over-fitting.However, **increasing the number of clusters (K) in the K-means** is not a strategy used to alleviate over-fitting. In fact, overfitting is generally a concern in supervised learning, while K-means clustering is an unsupervised learning technique.

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f the tollowing are the common strategies to alleviate (i.e. reduce) over-fitting ex

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

Section: Chapter Questions

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