From: Representation learning in intraoperative vital signs for heart failure risk prediction
Model | The parameter settings |
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Adaboost | n_estimators = 100 # The maximum number of estimators at which boosting is # terminated. |
Decision Tree (DT) | criterion = “gini” # The function to measure the quality of a split, supported criteria # are “gini” for the Gini impurity min_samples_split = 2 # The minimum number of samples required to split an # internal node. min_samples_leaf = 1 # The minimum number of samples required to be at a leaf # node. min_weight_fraction_leaf = 0.0 # The minimum weighted fraction of the sum total # of weights required to be at a leaf node. |
support vector machine (SVM) | kernel = “rbf” # Specifies the kernel type to be used in the algorithm. # “rbf” is Gaussian kernel function. |
logistic regression (LR) | penalty = “l2” # Specifies the norm used in the penalization, the ‘l2’ penalty is the # standard used in SVC. |
Random forest (RF) | n_estimators = 100 # The number of trees in the forest. |
Multiple perception machine (MLP) | alpha = 1e-5 # It is regularized parameters. hidden_layer_sizes = (5, 2) # The i-th element represents the number of neurons # in the i-th hidden layer. |
Gradient Boosting Decison Tree (GBDT) | n_estimators = 200 # The number of boosting stages to perform. |