Table 3.

Selected tuning parameters for prediction models

Sampling Framework			Logistic regression with LASSO		Random forest
Training/ test split	Cross-validation split	Model estimation	λ †	# Non-zero coefficients	Minimum terminal node size	# predictors sampled at each split‡
Visit	Visit	Observed cluster analysis	2.5 x 10−6	250	500	34
Visit	Person	Observed cluster analysis	4.5 x 10−5	86	5,000	8
Person	Person	Observed cluster analysis	4.5 x 10−5	105	5,000	17
Person	Person	Within cluster resampling	5.5 x 10−5	64‡	1,000	17

^†λ controls the degree of shrinkage for variable selection. A larger value of λ corresponds to more shrinkage, and a smaller value of λ, less shrinkage and more non-zero coefficients.

^‡The default recommendation for number of predictors randomly sampled for consideration at each split is square root of the total number of predictors, equal to 17 for our dataset. We also examined twice this default (34 predictors) and half of the default (8).

^§Average number of non-zero coefficients across logistic regression with LASSO models fit on 20 within cluster resampled datasets