Table 1.

Functions, Packages, and Tuning Parameters in the Anaconda Software Used for Each Machine Learning Algorithm.

Algorithm	Classifier	Package	Tuning Parameters
Logistic regression	LogisticRegression	from sklearn.linear_model import LogisticRegression	Penalty = “l2,” tol = 0.0001, C = 1, intercept_scaling = 1, max_iter = 100
DecisionTree	DecisionTreeClassifier	from sklearn.tree import DecisionTreeClassifier	splitter = “best,” max_depth = 2, min_samples_split = 20, min_samples_leaf = 5, min_weight_fraction_leaf = 0.1
forest	RandomForestClassifier	from sklearn.ensemble import RandomForestClassifier	n_estimators = 10, max_depth = 3, min_samples_split = 70, min_samples_leaf = 6, random_state = 41
GradientBoosting	GradientBoostinglassifier	from sklearn.ensemble import GradientBoostinglassifier	learning_rate = 0.06, n_estimators = 50, max_depth = 2, random_state = 41
gbm	lgb.LGBMClassifier	lightgbm 2.2.0	learning_rate = 0.1, n_estimators = 30, max_depth = 3

Note: gbm:(Light Gradient Boosting Machine).