# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 4 Methods  
One of the initial decisions to be made when utilizing machine learning techniques is how to conduct the learning process. In this study, all methods were employed with the supervised learning technique, given that the outputs of the dataset were available. Consequently, 70% of the available data in the training phase and 30% in the testing phase were utilized. Another issue that requires resolution is the selection of hyperparameter values. In this study, Bayesian optimization for hyperparameter tuning was used for this aim. The advantage of the Bayesian technique is that it performs a probabilistic scan, rather than calculating the effects of all hyperparameters. In other words, the Bayesian technique selects several hyperparameter settings, evaluates the selected parameters' quality, and decides which samples to select. Consequently, results can be obtained more rapidly. Finally, all the selected machine learning techniques were suitable for binary classification. In other words, all the techniques were suitable for the prediction of the status of aircrafts (late/on time). In the rest of this section, a brief introduction of each technique and their reason of selection will be presented. * Naive Bayes  
The Naive Bayes algorithm is a straightforward probabilistic classifier that calculates probabilities based on the frequency and combinations of values within a given dataset. The fundamental principle of this approach is the assumption of strong independence, which states that the presence or absence of any given class feature has no bearing on the presence or absence of any other feature. In essence, the Naive Bayes algorithm operates using conditional probability [50; 51]. One of its advantages is its ability to generate classifications from large datasets without the need for complex iterative parameter estimation procedures [52]. Because of this, it is chosen for its simplicity and effectiveness with large datasets, which makes it a good baseline model. * Artificial Neural Networks  
Artificial neural networks (ANNs) are designed to mimic the functionality of the human brain. Specifically, ANN models emulate the electrical activity of the brain and nervous system. Due to their structure, they can offer solutions to various problems, such as nonlinear and stochastic problems [53]. Artificial neurons or nodes, which are information processing units grouped in layers and joined by synaptic weights, constitute perceptron-type neural networks (connections). The technique has the advantages of working with incomplete data, being fault-tolerant, and not corrupting one or more cells in the network [54]. An ANN is chosen for its ability to capture complex, non-linear relationships in the data, as shown in [30; 32; 36]. * Logistic Regression  
Logistic regression is a well-established technique that was first developed by [PERSON] [55]. It is very sensitive to the problem of overfitting, which is usually fixed with the L2 arrangement [44]. This technique assumes that the relationship between the dependent variable and the independent variables is linear only between the logits of the explanatory variables and the output [56]. The method can be used when the dependent variable is only categorical. Unlike artificial neural networks, practitioners can see the relationship between variables and output. Another advantage is that they can find a solution in a short time, as less processing time is required [57]. Therefore, logistic regression is preferred for its interpretability and speed, which makes it suitable for quick insights and baseline comparisons, as seen in [35; 44]. * Random ForestsRandom Forests (RFs) are a model type that provides a transparent and understandable estimation since they are based on the idea that decision trees can be used in combination. It is a model introduced by [58]. The main idea of the Random Forests method is based on generating a large number of predictive trees and then randomly selecting sub-communities from the generated trees.