Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1-5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE O Model 2 Model 3 Model 5 O Model 4 O Model 1 1 <0.000001 0.955978 0.792802 18.277907 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 3 4 2 <0.000001 <0.000001 0.928791 0.928295 0.759583 0.761951 0.652212 17.253086 17.234646 14.639962 Test Data Scoring Models (Polynomial of order 1-5) 3 4 2 1.193874 1.764467 <0.000001 0.855568 1.358224 46.700141 -49.9795 68.2403 49.9795 5 <0.000001 0.832559 0.630168 13.863111 5 -162.8793 234.5554 162.8793 0.111347 0.941985 0.769574 13.310035 69851.1195 825243.8222
Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1-5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE O Model 2 Model 3 Model 5 O Model 4 O Model 1 1 <0.000001 0.955978 0.792802 18.277907 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 3 4 2 <0.000001 <0.000001 0.928791 0.928295 0.759583 0.761951 0.652212 17.253086 17.234646 14.639962 Test Data Scoring Models (Polynomial of order 1-5) 3 4 2 1.193874 1.764467 <0.000001 0.855568 1.358224 46.700141 -49.9795 68.2403 49.9795 5 <0.000001 0.832559 0.630168 13.863111 5 -162.8793 234.5554 162.8793 0.111347 0.941985 0.769574 13.310035 69851.1195 825243.8222
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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Transcribed Image Text:Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the
first 20 observations are assigned to the training set; the most recent 15 months are in the test set).
The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials
of order 1 - 5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is
the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training
data, and scored on both the training and test data sets. The results are below.
Based on this output, which is the best predictive model?
Metrics
AE
RMSE
MAE
SSE
Metrics
AE
RMSE
MAE
SSE
Model 2
Model 3
Model 5
Model 4
Model 1
1
<0.000001
0.955978
0.792802
18.277907
1
-1.034550
1.424155
1.208991
30.423248
Training Data Scoring
Models (Polynomial of order 1-5)
4
2
<0.000001
0.928791
0.759583
17.253086
3
<0.000001
<0.000001
0.928295
0.855568
0.761951
0.652212
17.234646 14.639962
Test Data Scoring
Models (Polynomial of order 1-5)
3
4
2
1.193874
1.764467
1.358224
46.700141
0.111347
0.941985
0.769574
13.310035 69851.1195
-49.9795
68.2403
49.9795
5
<0.000001
0.832559
0.630168
13.863111
5
-162.8793
234.5554
162.8793
825243.8222
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