## 4 Algorithm Bias
### Estimators

Table 6: _Cont._data from remote sensing are available (e.g., data coming from satellites), the authors test RF, ET and GBRT. Out of the three, ET performed the best in terms of accuracy, while GBRT performed the worst. In [124], XGBoost, LightGBM and GEP are studied in the context of estimating the quality of steel-fiber-reinforced concrete. Even though all of them generate adequate predictions, GEP is selected for forecasting the shear strength considering the most important factors, such as the beam depth effect. The authors in [125] experiment with CART, ANFIS, SVM and an MLP NN for performing time series forecasting using hydro-meteorological data. They conclude that the CART algorithm can be a very useful tool since it generates the most promising results in terms of the Nash-Sutcliffe model efficiency coefficient (i.e., NSE), Kling-Gupta efficiency (i.e., KGE) and Percent Bias (i.e., PBias). In another approach related to the environment [126], a fully connected NN, a CNN and an LSTM NN are trained on seismic data and the results showcase that the CNN and the LSTM NN were extremely effective both in terms of Mean Square Error (i.e., MSE) and statistical bias. In [127], the authors utilize a set of algorithms to predict whether an account holder will be able to repay financial loans in time. Those algorithms include DDQN, SVM, KNN, RF and XGBoost. Based on the results of this comparative study, KNN outperformed all the other algorithms in terms of prediction accuracy, whilst DDQN provided interesting results, paving the way for the application of other reinforcement learning techniques in similar use cases. With regard to the health domain, there have been several comparative studies that experiment on a diverse set of algorithms and datasets. Starting from [128], the authors compare LogR, an NN, SGD, RF, NB, KNN and DT by training them on a variety of open-source datasets concluding that LogR provided the best results based on a set of evaluation metrics that include, accuracy, precision, recall and F1-Score. In [129], the authors compare LinearSVC, LogR, MNB and RF in the context of identifying stereotypical patient characteristics in clinical science vignettes, concluding that LogR when combined with TF-IDF, can be quite useful for such use cases. In [130], the authors use Cox regression and an NN to predict the outcome of a COVID-19 infection. Even though both achieved high accuracy, the NN had significantly greater discriminatory ability. SVM, an NN and NB are tested in the context of [131], with regard to detecting leukemia and is showcased that SVM produced superior results in terms of classification accuracy. In [132], the authors make use of twenty-three (23) medical datasets and experiment with an NN, a DNN, C4.5, NB, KNN, LogR and SVM in order to find the most appropriate algorithm for medical datasets. Based on their results, SVM is the best-performing algorithm for medical datasets. Similarly, in [133] LogR, DT, RF and an NN are tested. NN had a greater accuracy but for a single class since it was affected by the bias present in the dataset. Taking this into consideration, the authors argue that RF performed the best with regard to all the classes present in the dataset. In [134], SVM and DT are compared. In this case, DT is slightly more accurate than SVM for diagnosing soft tissue tumors; however, it is more sensitive to the total number of parameters. As for medical images, the authors in [135] compare a non-recurrent NN and an LSTM NN, concluding that the LSTM-based approach improves the prediction accuracy, and the F1-Score compared to its non-recurrent counterpart. As for sentiment analysis, the authors in [136] experiment on a vast set of NN-based architectures, such as CNN-Bi-LSTM, LSTM, Bi-LSTM, CNN and GRU, as well as non-NN-based algorithms such as RF and SVM. According to their results, CNN-Bi-LSTM outperformed all the other algorithms in terms of accuracy.