Prior to consulting with your team, the analytics group at Greyson created a model to predict which customers are most likely to renew using linear regression with the LASSO shrinkage method. | This is incorrect. They should be using a classification model for this problem. This is correct because LASSO can help prevent overfitting with large numbers of predictor variables. This is in incorrect. With a large number of observations, they should use PCA for regression first and then fit the model to increase prediction accuracy.
Prior to consulting with your team, the analytics group at Greyson created a model to predict which customers are most likely to renew using linear regression with the LASSO shrinkage method. | This is incorrect. They should be using a classification model for this problem. This is correct because LASSO can help prevent overfitting with large numbers of predictor variables. This is in incorrect. With a large number of observations, they should use PCA for regression first and then fit the model to increase prediction accuracy.
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
Problem 1PE
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![**Question:**
Prior to consulting with your team, the analytics group at Greyson created a model to predict which customers are most likely to renew using linear regression with the LASSO shrinkage method.
- [X] This is incorrect. They should be using a classification model for this problem.
- [ ] This is correct because LASSO can help prevent overfitting with large numbers of predictor variables.
- [ ] This is incorrect. With a large number of observations, they should use PCA for regression first and then fit the model to increase prediction accuracy.
In this example, the correct answer is marked as:
- This is incorrect. They should be using a classification model for this problem.
The linear regression with LASSO shrinkage method may not be appropriate for predicting whether customers will renew, as this typically is a classification problem rather than a regression problem. Classification models are better suited for predicting categorical outcomes.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F729479f6-1c19-463a-a537-8081d0281230%2Fd3bfb54c-4e57-4b8b-b5ed-20fb5026bbde%2F47akax_processed.png&w=3840&q=75)
Transcribed Image Text:**Question:**
Prior to consulting with your team, the analytics group at Greyson created a model to predict which customers are most likely to renew using linear regression with the LASSO shrinkage method.
- [X] This is incorrect. They should be using a classification model for this problem.
- [ ] This is correct because LASSO can help prevent overfitting with large numbers of predictor variables.
- [ ] This is incorrect. With a large number of observations, they should use PCA for regression first and then fit the model to increase prediction accuracy.
In this example, the correct answer is marked as:
- This is incorrect. They should be using a classification model for this problem.
The linear regression with LASSO shrinkage method may not be appropriate for predicting whether customers will renew, as this typically is a classification problem rather than a regression problem. Classification models are better suited for predicting categorical outcomes.
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