## 2 Theoretical Background of Applied Method
### Advantages and Disadvantages of Machine Learning Methods  
Learning algorithms work based on strategies, algorithms, and inferences that have worked well in the past and are likely to work well in the future. Individual machine learning methods differ in algorithms and advantages and disadvantages, which can be decisive when applying the chosen method. Table 3 shows a comparison of individual methods in terms of their general properties. In general, the advantage of SVM regression is that its efficiency increases with the size of the input data. The disadvantage of the SVR model is that the learning ability may decrease with increasing samples. Another disadvantage is that to achieve the model's required performance is necessary to set a suitable kernel function. The SVR model is challenging to understand and interpret because the learned model maintains a database or program structure of learned support vectors used for prediction. The risk of overfitting is lower in the case of SVR than in the case of NN. The advantage of NNs is their ability to learn and make decisions based on similar events. The disadvantage of NNs is their hardware dependency, limiting a network learning target prediction to one computer. All learned information is maintained in a NN instead of a database. Other disadvantages include the need for an experimental setup \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{SVR} & Can utilize predictive power of linear combinations of inputs. & \\  & Works well outside of training data. & \\  & A good solution for regression on nonlinear data. & \\  & Not prone to overfitting. & \\  & Durable to noise. & \\  & Low generalization error. & \\ \hline \multirow{6}{*}{NN} & Difficult to understand structure of algorithm. & \\  & & Computationally expensive and prone to overfitting. \\  & & Needs a lot of training data, often much more \\  & Tolerant to noise and missing data. & \\  & A good solution for regression on nonlinear data. & \\  & Extensive literature. & \\  & Good prediction, generally. & \\  & Some tolerance to correlated inputs. & \\  & Incorporating the predictive power of different combinations of inputs. & \\  & & Not robust to outliers. \\  & & Susceptible to irrelevant features. \\  & & Difficult in dealing with big data with a complex model. \\ \hline \multirow{6}{*}{MARS} & Works well with many predictor variables. & Susceptible to overfitting. \\  & Automatically detects interactions between variables. & \\  & It is an efficient and fast algorithm, despite its complexity. & \\  & MARS naturally handles mixed types of predictors (quantitative and qualitative). & \\  & Robust to outliers. & \\  & Ability to model large datasets more flexibly than & \\  & linear models. & \\  & The final regression model can be portable to various hardware. & \\  & Automatically models non-linearities and interactions between variables. & \\ \hline \multirow{6}{*}{\(k\)-NN} & Simple, adaptable to the problem. & Memory intensive. \\  & Accurate. & Costify, all training data \\  & Easy to understand. & might be involved in decision making. \\  & Uses spatial trees to improve space issues. & Slow performance due to I/O operations. \\  & Nonparametric. & Selection of the optimal number \\  & Intuitive approach. & of neighbors can be problematic. \\  & Robust to outliers on the predictors. & \\  & Zero cost in the training process. & \\ \hline \multirow{6}{*}{\(k\)-NN} & Not difficult to understand. & \\  & High accuracy. & \\  & A good starting point to solve a problem. & \\  & Flexible and can fit a variety of different data well. & Overfitting. \\ \cline{1-1}  & Fast to execute. & Not suitable for small samples. \\ \cline{1-1}  & Easy to use. & \\ \cline{1-1}  & Useful to regression and classification problems. & \\ \cline{1-1}  & Can model missing values. & \\ \cline{1-1}  & High performing. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of advantages and disadvantages of machine learning methods.