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Table 2 Model hyper-parameters of the classification models

From: Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

Classification models

Hyper-parameter search spaces

Neural network (NN)

hidden_layer_sizes = {100, 200, 300}, learning_rate = 0.001

Support vector machines (SVM)

C = {0.01, 0.1, 1, 5, 10, 100}, kernel = {‘linear’, ‘rbf’}, gamma = {0.01, 0.1, 1, 10}, class_weight = {None, ‘balanced’}

k-nearest neighbor (kNN)

n_neighbors = {1,3,5,7,9}, weights = {‘uniform’, ‘distance’}

Decision tree (DT)

max_depth = {5, 6, 7, 8, 9, 10, 15, 20}, class_weight = {None, ‘balanced’}

Random forest (RF)

n_estimators = {20, 50, 100, 200}, class_weight = {None, ‘balanced’, ‘balanced_subsample’}

Gradient boosting (GB)

n_estimators = {20, 50, 100, 200}

Logistic regression (LR)

C = {0.01, 0.1, 1, 5, 10, 100}, penalty = {‘l1’, ‘l2’}, class_weight = {None, ‘balanced’}

  1. Note: Adam was used for learning rate optimization of NN [43]; gamma hyper-parameter in SVM was applied when the kernel is radial basis function ‘rbf’; class_weight was applied when the oversampling approach (SMOTE) was not used. Further details about hyper-parameters used in this study can be found: NN (https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html), SVM (https://scikit-learn.org/stable/modules/generated/sklearn.svm. LinearSVC.html#sklearn.svm.LinearSVC),kNN (https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html), DT (https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html), RF (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), GB (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html), and LR (https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)