Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

Algorithm 2: Summary of RF model architecture [39].

	Number of observations used to build the model: 33
	Missing value imputation is active.
	Call:
	RandomForest (formula = 91 days compressive strength ∼.,
	data = crs$dataset[crs$sample, c(crs$input, crs$target)],
	ntree = 500, mtry = 2, importance = TRUE, replace = FALSE, na.action = randomForest::na.roughfix)
	Type of RF: regression
	Number of trees: 500
	No. of variables tried at each split: 2
	Mean of squared residuals: 2.360152
	% Var explained: 91.58

Algorithm 2: Summary of RF model architecture [39].