Table 4.

Functions, packages, and tuning parameters used in Anaconda for each machine learning algorithm.

Algorithm	Classifier	Package	Tuning parameters
Logistic regression	LogisticRegression	from sklearn.linear_model import LogisticRegression	penalty =‘l2’, tol = 0.000001, C = 0.1, fit_intercept = True,intercept_scaling = 1, class_weight = None,max_iter = 100, multi_class =‘ovr’,verbose = 0, warm_start = False,n_jobs = 1
DecisionTree	DecisionTreeClassifier	from sklearn.tree import DecisionTreeClassifier	splitter =‘best’, max_depth = 3, min_samples_split = 30, min_samples_leaf = 2, min_weight_fraction_leaf = 0.01
forest	RandomForestClassifier	from sklearn.ensemble import RandomForestClassifier	n_estimators = 50, n_jobs = -1, min_samples_split = 20, min_samples_leaf = 2, random_state = 41
GradientBoosting	GradientBoostinglassifier	from sklearn.ensemble import GradientBoostinglassifier	learning_rate = 0.2, n_estimators = 20, max_depth = 3, min_samples_split = 20, min_samples_leaf = 5
gbm	lgb.LGBMClassifier	lightgbm 2.2.0	boosting_type =‘gbdt’, objective =‘binary’,metrics =‘auc’,learning_rate = 0.1, n_estimators = 100, max_depth = 2, bagging_fraction = 0.5, feature_fraction = 0.5