## 2 Peculiarities of Implemented ML Pipelines for IR and THz Absorption Spectra Analysis
### Regression  
A summary of regression methods applications in gas sample substances concentration estimation is given in Table 3 [61; 79; 80; 81; 82; 83]. These methods are mostly linear and do not always show good model performance on complex multivariate data, which include IR and THz spectra. A viable option is multivariate methods with regularization, for example, LASSO (least absolute shrinkage and selection operator), which also provides a selection of variables to remove redundant and noisy data by setting regression coefficients in the model to zero [84; 85]. ### Regression  
A summary of regression methods applications in gas sample substances concentration estimation is given in Table 3 [61; 79; 80; 81; 82; 83]. These methods are mostly linear and do not always show good model performance on complex multivariate data, which include IR and THz spectra. A viable option is multivariate methods with regularization, for example, LASSO (least absolute shrinkage and selection operator), which also provides a selection of variables to remove redundant and noisy data by setting regression coefficients in the model to zero [84; 85]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Idea** & **Advantages** & **Disadvantages** & **Interpretability** & **Ref.** \\ \hline GB & A sequence of decision tree models, where each successor is focused on correcting prediction errors of predecessor. & High model performance, fast learning. & High model hyperparameters to tune up. Regularization does not always prevent overfitting. & GB except for some versions can be interpreted. & [73] \\ \hline K-NN & Formally, the basis of the method is the compactness hypothesis: if the metric of the distance in a feature space is introduced successfully, then neighbor samples are much more often in the same class than in different ones. & The number of nearest neighbors (k) is chosen beforehand and can affect the accuracy of classification. Low performance, dependence on the similarity metrics. & K-NN model can be interpreted. & [74; 75; 76] \\ \hline Soft independent modeling by class analogy (SIMCA) & Based on PCA decomposition of the spectral data. Only data with high explained variance value remained. & Can be used with different similarity metrics, robust to noise. & Same as for PCA. & Like PCA can be interpreted. & [77; 78] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Idea** & **Advantages** & **Disadvantages** & **References** \\ \hline LRA & Output variable approximated as linear combination of input ones (predictors). LRA is an extension of regression analysis for the case of categorical outcome variables. & Fast learning, interpretable model. & Suitable for linear data only. Sensitive to outliers, noise and prone to overfitting. & [79] \\ \hline PCR & This method uses PCA to transform data into PC space and after that a linear regression is constructed. & Model can be interpreted by loadings matrix analysis. The only one tunable parameter. & It is hard to choose the appropriate value of PCs. The limitations are the same as for PCA. & [61] \\ \hline PLSR & This method projects predictors and output variables into a new latent space and after that uses LRA. & Simple, interpretable, fast learning. & There is a risk of misinterpretation. Sensitive to the scaling of predictors. & [80] \\ \hline SVR & Aims to find a hyperplane, which fits the data points while minimizing margin violations, capturing patterns and relationships in the data by passing through as many data points as possible within a specified margin. & Good generalization ability. Linear model can be interpreted. & Separates slightly differing spectra. Provides fair results when number of initial variables is much greater than number of spectra. & [81; 82; 83] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression methods.