The following figure depicts the decision boundary for the logistic regression model built to predict preference for light beer vs. regular beer (PREFERENCE = Light is the success or positive class). The explanatory variables are INCOME and AGE. INCOME has a positive coefficient and AGE has a negative coefficient. Predictions are made based on the depicted decision boundary. Four observations are circled. Match them with the appropriate descriptions. Age (in years) A B C D 100 8 60 40 20 -20 $10 $20 $30 Beer Preference $40 $50 $60 Income (in US$ thousands) L.1-TJ B LJ $70 LAT A False negative True positive True negative False positive $80 $90 ◆ Regular ●Light <

The following figure depicts the decision boundary for the logistic regression model built to predict preference for light beer vs. regular beer (PREFERENCE = Light is the success or positive class). The explanatory variables are INCOME and AGE. INCOME has a positive coefficient and AGE has a negative coefficient. Predictions are made based on the depicted decision boundary. Four observations are circled. Match them with the appropriate descriptions. Age (in years) A B C D 100 8 60 40 20 -20 $10 $20 $30 Beer Preference $40 $50 $60 Income (in US$ thousands) L.1-TJ B LJ $70 LAT A False negative True positive True negative False positive $80 $90 ◆ Regular ●Light <

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 48. Let us return to the Titanic data set. We now have learned several models and want to choose the best one. We used three different methods to validate these models: The training error rate (apparent error rate), the error rate on an external test set and the error rate estimated by a 10-fold cross validation. Training Error | Error on the test set | Cross Validation Error 0.18 Learner Decision Tree 0.22 0.21 Random Forest 0.01 0.10 0.12 1-Nearest-Neighbour 0.18 0.19 Which of the following statements are correct? a) 1-Nearest-Neighbour has a perfect training error and hence it should be used here. b) Random Forests outperforms both 1-Nearest-Neighbour and the Decision Tree in terms of prediction error. c) Not just in this case, but in general, Cross Validation is the better validation strategy and should always be preferred over the error on a single test set. d) Not just in this case, but in general, Decision Trees always perform worse than Random Forests.
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