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Table 8 Grid search results of the classifiers for module 2, with and without L 0 penalization

From: Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism

Classifier

Linear reg

Lasso

Ridge

Elastic net

Relaxed Lasso

L1 logreg

L2 logreg

LDA

L 0 penalized ROC AUC

73.7

84.1

75.4

84.6

84.3

83.3

75.4

86.5

Associated real ROC AUC

80.0

84.9

81.5

85.5

85.1

84.2

85.4

87.4

Features used with L 0

36.5

4.4

35.7

5.3

4.4

5.4

58.0

5.0

Not penalized ROC AUC

79.3

85.1

81.2

86.3

85.3

85.9

85.3

87.4

Features used without L 0

35.4

4.5

34.6

10.7

4.5

4.0

58.0

5.0

Classifier

pSVM

rSVM

eSVM

L1 lSVM

Grad Boost

AdaBoost

Rand Forest

Tree

L 0 penalized ROC AUC

86.8

86.1

49.3

81.2

85.7

85.7

86.3

82.4

Associated real ROC AUC

87.7

86.6

0.50

81.6

86.6

86.6

86.2

83.0

Features used with L 0

5.0

5.0

4.0

2.4

5.0

5.0

5.0

3.0

Not penalized ROC AUC

87.6

85.9

50.0

83.0

86.6

86.6

88.4

83.0

Features used without L 0

5.0

12.0

4.0

29.4

5.0

5.0

28.9

3.0

  1. pSVM, rSVM, eSVM, and lSVM correspond to different kernels for SVM (polynomial, radial, exponential, and linear) and logreg to logistic regression. Italicized data points highlight the worst performing models (too many features used and/or poor performance)