Table 1 Summary table of performance measures of the investigated ML algorithms developed on human expert-annotated features (HEAF).

From: Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings

Report section	Method	ML Classifier	HEAF feature space	Rank	Software	Optimized metric	Tested hyperparameter space	Selected number of features or hyperparameter settings on outer fold 1.0–5.0	Accuracy^# [min–max; %]	ME	AUC	BS	LL
Human Expert-Annotated Features (HEAF)	CART	CT	p = 28 (all)		rpart [R]	ACC	rpart.control = default; cp = 0.01 no optimization (no pruning)	28	73.3 [66.7–79.2]	0.27	0.63	0.37	0.87
	vRF	RF	p = 28 (all)	4	randomForest [R]	ME	ntree = 500, mtry = 5, p_varsel = 28	28	81.5 [73.8–92.7]	0.18	0.82	0.27	0.44
	vRF	RF	p = 28 (all)		randomForest[R]	ME	ntree = 500, mtry = 5, p_varsel = 9	9	71.0 [59.5–82.9]	0.29	0.69	0.37	0.56
	vRF	RF	p = 28 (all)		randomForest[R]	ME	ntree = 500, mtry = 5, p_varsel = 5	5	75.2 [68.3–83.3]	0.25	0.69	0.36	0.54
	tRF_BS	RF	p = 28 (all)	2	randomForest[R]	BS	ntree = [100, 200, 300, … , 900, 1000]	28, 14, 14, 14, 14	83.1 [76.2–90.2]	0.17	0.81	0.27	0.44
	tRF_ME	RF	p = 28 (all)		randomForest[R]	ME	mtry = [3, 4, 5, 6, 7]	28, 28, 14, 5, 14	79.6 [68.3–90.2]	0.20	0.79	0.29	0.46
	tRF_LL	RF	p = 28 (all)	2	randomForest[R]	LL	p_varsel = [3, 5, 10, 14, 20, 25, 28]	25, 14, 14, 14, 14	83.1 [76.2–90.2]	0.17	0.81	0.27	0.44
	ELNET	ELNET	p = 28 (all)	3	glmnet[R]	ME	α = [0, 0.1, 0.2, … , 0.8, 0.9, 1] λ = tenfold CV with default hot-start	α = [0.1, 0.8, 0, 1, 0.1] λ = [0.195, 0.0688, 0.208, 0.0301, 0.1632]	82.0 [78.6–85.4]	0.18	0.79	0.27	0.43
	SVM-LK	SVM	p = 28 (all)	1	e1071[R]	ME	C = [0.001, 0.01, 0.1, 1, 10, 100, 1000]	C = [1, 1, 100, 10, 10]	87.4 [82.9–90.2]	0.13	0.79	0.22	0.37
	XGBoost	BT	p = 28 (all)	5	xgboost[R]	ME	nrouds/ntree = 100,	nrouds = 100	80.6 [75.0–85.7]	0.19	0.70	0.30	0.48
							max_depth = [3, 5, 6, 8]	max_depth = [5, 3, 5, 8, 3]
							eta = [0.1, 0.3]	eta = [0.1, 0.1, 0.1, 0.3, 0.1]
							gamma = [0, 0.5, 1.0]	gamma = [0, 0.5, 1, 0.5, 1]
							colsample_bytree = [0.1, 0.25, 0.5, 0.693 (ln2) ~ ^RF, 1]	colsample_bytree = [1, 1, 0.5, 1, 0.5]

Accuracy#: the averaged fivefold CV accuracy is calculated, ACC: accuracy, AUC: multiclass area under the ROC after Hand and Till (that can only be calculated if probabilities are scaled to 1), BS: Brier score, ME: misclassification error, LL: multiclass log loss, vRF and tRF: vanilla- and tuned random forests, ELNET: elastic net penalized multinomial logistic regression, SVM: support vector machines, LK: linear kernel SVM; XGBoost: extreme gradient boosting using trees as base learners, BT: boosted trees, CART: classification and regression trees; CT: classification tree; cp: complexity parameter used for CART node splitting (for this no optimization (pruning) was performed); ln(2) ~ ^RF: column sampling (i.e. bootstrap) representing the settings equivalent to running RF in the xgboost library, [R]: R statistical software environment.

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Table 1 Summary table of performance measures of the investigated ML algorithms developed on human expert-annotated features (HEAF).

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