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Table 1 PFI, BIC and SHAP success in identification of feature ranks in datasets with two-way and three-way epistatic interactions. It is expressed as the percentage of a match of a metric rank’s estimate with the true feature rank that was retrieved with the HIBACHI sensitivity analysis

From: A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions

Sample size 1000

Two-Way IG

Three-Way IG

% of cases:

25%

50%

25%

50%

Metrics:

PFI

BIC

SHAP

PFI

BIC

SHAP

PFI

BIC

SHAP

PFI

BIC

SHAP

 F1

70%

41%

71%

91%

42%

82%

80%

38%

57%

79%

18%

68%

 F2

63%

41%

62%

90%

42%

82%

69%

33%

45%

60%

36%

52%

 F3

93%

84%

89%

78%

82%

81%

79%

33%

53%

71%

18%

73%

 F4

17%

17%

15%

15%

17%

16%

74%

55%

58%

11%

14%

11%

 F5

5%

4%

5%

4%

5%

3%

33%

31%

29%

5%

6%

4%

Sample size 10,000

Two-Way IG

Three-Way IG

% of cases:

25%

50%

25%

50%

Metrics:

PFI

BIC

SHAP

PFI

BIC

SHAP

PFI

BIC

SHAP

PFI

BIC

SHAP

 F1

79%

36%

73%

83%

39%

75%

89%

42%

68%

86%

19%

76%

 F2

79%

32%

73%

83%

39%

75%

80%

34%

49%

75%

34%

56%

 F3

100%

87%

99%

81%

79%

81%

89%

33%

66%

85%

22%

70%

 F4

13%

12%

13%

44%

38%

40%

84%

62%

69%

53%

51%

52%

 F5

5%

4%

5%

15%

13%

14%

40%

38%

37%

11%

9%

10%

  1. F1, F2, etc. – feature ranks, PFI permutation feature importance, BIC build-in coefficients, SHAP shapley additive explanations, IG information gain