Fig. 2: Feature selection utilizing LASSO regression and evaluation of feature importance by using random forest methodology. | npj Parkinson's Disease

Fig. 2: Feature selection utilizing LASSO regression and evaluation of feature importance by using random forest methodology.

From: Construction of a mild cognitive impairment prediction model for Parkinson’s disease patients on the basis of multimodal data

Fig. 2

a, d, g, j illustrate the coefficient path plots for clinical data, gait, eye tracking and gFCD, respectively. b, e, h, k represent binomial deviations for clinical data, gait, eye tracking, and gFCD, respectively. c, f, i, l represent random forest algorithms for clinical data, gait, eye tracking, and gFCD, respectively.

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