Table 2 Description of LSA input parameters

ADA

Adaptive boosting

Boosting basic algorithm initially generates a weak learner and weights it sequentially

Freund and Schapire (1997)

CatBoost

Categorical boosting

Combines boosting with 'target encoding' to improve categorical data processing performance that was vulnerable in traditional boosting algorithms

Prokhorenkova et al. (2018)

DT

Decision tree

Classifies variables into nodes based on classification criteria and recursively classifies the process

Dummy

Dummy classifier

A simple comparison between training and prediction data. Builds a baseline for model performance comparison, but not for actual prediction

ET

Extremely randomized tree

Uses the entire data without a bagging process in the structure of RF and randomly generates node branches

Geurts et al. (2006)

GBC

Gradient boosting

Boosting basic algorithm to predict the residual of the previous step sequentially from the initial weak learner

Friedman et al. (2001)

KNN

K nearest neighbors

Determined by a majority vote of the nearest k data of the target

LDA

Linear discriminant analysis

Assumes that all classes share the same covariance matrix, i.e., have a linear structure, by applying a Bayes rule that maximizes the probability that a given data belongs to each class

lightGBM

Light gradient boosting

While other GBC algorithms apply tree depth minimization through the levelwise method, only certain trees are developed through the leafwise method to minimize loss and shorten the time

Ke et al. (2017)

NB

Naive Bayes

Classified on a Bayes basis with the simple assumption that all characteristics are independent of each other

Lewis (1998)

QDA

Quadratic discriminant analysis

Unlike LDA, assumes that each class is a different covariance matrix. Therefore, the crystal boundary is in the form of a quadratic curve

RF

Random forest

Samples data through the bootstrap process to perform prediction and aggregation with multiple decision trees; allows to measure the importance of each data

Breiman (2001)

Ridge

Ridge classifier

Performed based on linear regression methods, but adds a normalization process called 'L2 regularization' to avoid overfitting

SVM

Support vector machine

Distinguished by hyperplane between the two data, and regression is also possible based on hyperplane. Basic 'linear model' and 'RBF kernel model considering multidimensional data' are used

Cortes and Vapnik (1995)

XGBoost

Extreme gradient boosting

Improves performance through normalization, pruning, and missing value processing in traditional GBC

Chen et al. (2016)