From: A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
ML models | Descriptions | Main parameter settingsa |
---|---|---|
RF | A meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting | n_estimators = 100, max_features = 0.5, min_samples_split = 4, min_samples_leaf = 10, criterion = "entropy" |
SVM | Solves the separation hyperplane which can divide the training data set correctly and has the maximum geometric interval | penalty = "l2", loss = "squared_hinge", tol = 1e−5, C = 5.0, max_iter = 1e + 5 |
LR | Estimates the probability of an event occurring based on a given dataset of independent variables | tol = 1e−5, C = 5.0, max_iter = 1e + 4 |
XGB | A boosting algorithm based on gradient boosted decision trees algorithm | booster: "gbtree", max_depth: 8, n_estimators: 100, min_child_weight: 3, gamma: 0.15, lambda: 2 |
DT | Employs a divide and conquer strategy by conducting a greedy search to identify the optimal split points within a tree | criterion = "gini", splitter = "best", min_samples_split = 2, min_samples_leaf = 1 |