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Table 5 Averaged feature ranking across models and specifications without directional priors

From: Cross-border mobility responses to COVID-19 in Europe: new evidence from facebook data

 

Panel A: Corridor & Day dummies

Panel B: Corridor dummies

Features

Linear

KNN

G-Boost

MLP

Avg.

Linear

KNN

G-Boost

MLP

Avg.

C1 School closures

100%

78%

98%

91%

93%

100%

100%

86%

86%

93%

C3 Cancel public events

14%

100%

79%

84%

69%

21%

100%

60%

76%

64%

C6 Stay home requirement

56%

18%

78%

71%

56%

25%

23%

64%

38%

38%

C7 Restr. Internal movement

6%

97%

20%

100%

56%

1%

84%

27%

100%

53%

C4 Restrict gatherings

28%

60%

80%

49%

54%

9%

76%

65%

64%

53%

H2 Testing policy

11%

4%

64%

64%

36%

26%

2%

49%

30%

36%

New Covid deaths

3%

10%

100%

0%

28%

0%

77%

100%

0%

44%

C8 Inter. travel controls

0%

8%

47%

44%

25%

3%

17%

44%

32%

24%

H3 Contact tracing

18%

20%

20%

35%

23%

12%

1%

19%

32%

16%

New Covid cases

11%

0%

46%

35%

23%

11%

76%

63%

87%

59%

C5 Close public transport

26%

7%

0%

43%

19%

15%

0%

0%

31%

11%

C2 Workplace closing

2%

34%

12%

21%

17%

2%

46%

23%

33%

26%

  1. Notes: The different features are ranked following the permutation importance method. For each approach, we provide results obtained with the model including day/corridor dummies (cols. 1-5) and the version including corridors dummies only (cols. 6-10). Directional priors are not included. The importance values of each feature is computed over 10 permutations using the negative mean absolute error (MAE). The origin- and destination-specific features importance are aggregated by taking the mean between the 2. Finally, the resulted values are scaled between 0% and 100% separately for each model. The last column in each panel presents the mean value of importance averaged over the four models. The features are ranked according to the average importance of the models including the corridor and day dummies