You decide to run a simpler model to predict churn, using only the variables tenure (in months) and TotalCharges (in US$). The output is given below. The AIC of this model is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which model would be expected to give superior predictive performance?

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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You decide to run a simpler model to predict churn, using only the variables tenure (in
months) and TotalCharges (in US$). The output is given below. The AIC of this model
is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which
model would be expected to give superior predictive performance?
Actual
## Coefficients:
##
Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.471e-01 5.360e-02 4.611 4.01e-06 ***
## tenure
< 2e-16 ***
-1.124e-01 5.816e-03 -19.334
## TotalCharges 8.236e-04 5.618e-05 14.660
< 2e-16 ***
##
No
---
## Signif. codes: 0
##
Yes
Yes
##
Null deviance: 5701.5 on 4921
## Residual deviance: 4721.6 on 4919
## AIC: 4727.6
515
345
## (Dispersion parameter for binomial family taken to be 1)
##
Predicted
*****
No
795
3267
0.001
Confusion Matrix (Training)
****
Actual
0.01
Yes
No
degrees of freedom
degrees of freedom
Yes
The simpler model (with just tenure and TotalCharges)
The full model (with all variables)
0.05 0.1
220
145
Predicted
No
339
1406
Confusion Matrix (Test)
Transcribed Image Text:You decide to run a simpler model to predict churn, using only the variables tenure (in months) and TotalCharges (in US$). The output is given below. The AIC of this model is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which model would be expected to give superior predictive performance? Actual ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.471e-01 5.360e-02 4.611 4.01e-06 *** ## tenure < 2e-16 *** -1.124e-01 5.816e-03 -19.334 ## TotalCharges 8.236e-04 5.618e-05 14.660 < 2e-16 *** ## No --- ## Signif. codes: 0 ## Yes Yes ## Null deviance: 5701.5 on 4921 ## Residual deviance: 4721.6 on 4919 ## AIC: 4727.6 515 345 ## (Dispersion parameter for binomial family taken to be 1) ## Predicted ***** No 795 3267 0.001 Confusion Matrix (Training) **** Actual 0.01 Yes No degrees of freedom degrees of freedom Yes The simpler model (with just tenure and TotalCharges) The full model (with all variables) 0.05 0.1 220 145 Predicted No 339 1406 Confusion Matrix (Test)
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