## 4 Algorithm Bias
### Estimators
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm (Estimator) Name** & **Abbreviation** & **Description** \\ \hline Bidirectional Long Short-Term Memory Neural Network & Bi-LSTM NN & Specific type of LSTM neural network that enables additional training by traversing the input data [80] \\ \hline Cascade Support Vector Machine & C-SVM & Ensemble ML technique that consists of several SVMs stacked in a cascade [81] \\ \hline C4.5 Classifier & C4.5 & Type of a decision tree classifier [82] \\ \hline Classification and regression tree & CART & Type of a decision tree algorithm that can be used for both classification and regression tasks [83] \\ \hline CNN-Bi-LSTM & CNN-Bi-LSTM & Type of a Bi-LSTM NN that also utilizes a convolutional layer [84] \\ \hline Conditional Random Fields & CRF & Statistical modeling method that is mostly used in pattern recognition and ML for structured predictions, since it models predictions into graphs [85] \\ \hline Convolution Long Short-Term Memory & CLSTM & Type of an LSTM NN that also utilizes a convolutional layer [86] \\ \hline Convolutional Neural Network & CNN & A type of feed-forward NN that consists of at least one convolutional layer and is widely used in computer vision tasks [87] \\ \hline Cox proportional hazards model & Cox regression & Type of a survival model. That kind of models try to associate time prior to one event happening with one or more covariates [88] \\ \hline Cubist & Cubist & Rule-based model that contains a tree whose final leaves use linear regression models [89] \\ \hline Decision Trees & DT & Fundamental ML algorithm that uses a tree-like model of decisions and can be used for both regression and classification tasks [90] \\ \hline Deep Neural Network & DNN & A type of an NN that has multiple hidden layers between the input and the output [91] \\ \hline Double Deep Q-Network & DDQN & Consists of two separate Q-networks that are a type of reinforcement learning algorithms [92] \\ \hline Exponential smoothing methods & ESM & Method used in time series forecasting that utilizes an exponentially weighted average of past values to predict a future value [93] \\ \hline Extra Trees & ET & An ensemble ML approach that utilizes multiple randomized decision trees [94] \\ \hline Extreme Gradient Boosting & XGBoost & Iteratively combines predictions of multiple individual models, usually DTs [95] \\ \hline Gene Expression Programming & GEP & Type of evolutionary algorithm that consist of complex trees that adapt and alter their structures [96] \\ \hline Gradient Boosted Regression Trees & GBRT & Type of additive model that makes predictions by combining decisions from a set of other models [97] \\ \hline Gated Recurrent Unit Networks & GRU & Type of Recurrent Neural Network, similar to LSTM, that utilize less parameters, thus having less computational cost [98] \\ \hline k-Nearest Neighbors & KNN & A fundamental ML algorithm used for classification and regression based on proximity of data points [99] \\ \hline Light Gradient Boosting Machine & LightGBM & Ensemble method that is based on gradient boosting [100] \\ \hline Linear Discriminant Analysis & LDA & Supervised ML algorithm for classification that aims to identify linear set of features that identify classes into a dataset [101] \\ \hline Linear Regression & LinearR & Fundamental algorithm that estimates the linear relationship between two variables [102] \\ \hline Linear Support Vector Classification & LinearSVC & Subtype of SVM used for perfectly linear data [103] \\ \hline Logistic Regression & LogR & Used for estimating the parameters of logistic models [104] \\ \hline Long-Short Term Memory & LSTM & Type of recurrent neural network that is particularly useful for time series forecasting, since it is capable to “remember” [105] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._Having presented the algorithms that will be discussed in this section, the corresponding comparative studies are now going to be analyzed.