You decide to run a simpler model to predict churn, using only the variables tenure (inmonths) and TotalCharges (in US$). The output is given below. The AIC of this modelis 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this whichmodel 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##YesYes##Null deviance: 5701.5 on 4921## Residual deviance: 4721.6 on 4919## AIC: 4727.6515345## (Dispersion parameter for binomial family taken to be 1)##Predicted*****No79532670.001Confusion Matrix (Training)****Actual0.01YesNodegrees of freedomdegrees of freedomYesThe simpler model (with just tenure and TotalCharges)The full model (with all variables)0.05 0.1220145PredictedNo3391406Confusion Matrix (Test)

AIC: AIC stands for Akaike Information Criterion, which is a statistical measure used to compare…

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?

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

Problem 1PE

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Concept explainers

Temporal difference learning

Temporal difference learning (TD learning) is a concept in machine learning that attributes to a class of model-free reinforcement learning methods. The technique predicts a reward or quantity depending on the future values of the value function.

Question

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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