# A Machine Learning Approach for the Classification of Refrigerant Gases
## 5 Evaluation
### Data Visualization  
As high-dimensional datasets can be difficult to interpret and comprehend. Visualizing high-dimensional data often results in cluttered and overlapping visual elements, leading to a loss of interpretability and an inability to discern meaningful patterns [38]. By reducing the number of dimensions, these techniques facilitate the representation of complex data in a lower-dimensional space, making it easier to visualize and understand the underlying relationships between variables. Moreover, dimensionality reduction can reveal the inherent structure of the data, helping researchers and practitioners identify clusters, trends, and outliers that may not be apparent in the original high-dimensional space. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline R32 0\% & 0.5000 & 0.5000 & 0.5000 \\ R32 5\% & 0.5000 & 0.5000 & 0.5000 \\ R32 10\% & 0.7500 & 0.7500 & 0.7500 \\ R32 25\% & 0.5714 & 1.0000 & 0.7273 \\ R32 50\% & 0.0000 & 0.0000 & 0.0000 \\ R32 70\% & 0.7500 & 0.7500 & 0.7500 \\ R134a 0\% & 1.0000 & 0.5000 & 0.6667 \\ R134a 5\% & 1.0000 & 1.0000 & 1.0000 \\ R134a 10\% & 1.0000 & 1.0000 & 1.0000 \\ R134a 25\% & 1.0000 & 0.7500 & 0.8571 \\ R134a 50\% & 0.5714 & 1.0000 & 0.7273 \\ R134a 70\% & 0.8000 & 1.0000 & 0.8889 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{Accuracy} & 0.7292 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification Performance Metrics. Dimensionality reduction techniques include t-distributed stochastic neighbor embedding (t-SNE) [39]. Visualizing data using t-SNE is particularly valuable for signal visualization because it can preserve the essential characteristics of the data while reducing noise and redundancy. By focusing on the most significant components of the data, these methods enhance the clarity and interpretability of visualizations, allowing researchers to gain a deeper understanding of the underlying signal dynamics. Furthermore, dimensionality reduction can also aid in the pre-processing stage of machine learning algorithms by addressing the curse of dimensionality. This phenomenon arises when high-dimensional datasets suffer from sparse sampling, leading to decreased model performance and longer training times. In summary, dimensionality reduction techniques are essential for signal visualization as they improve interpretability, reveal hidden structures in the data, and address the challenges associated with high-dimensional datasets. By leveraging these techniques, researchers and practitioners can effectively explore and analyze complex signal data, ultimately leading to more informed decisions and better insights into the underlying phenomena. Figure 4 displays the t-SNE projection of the feature space for two refrigerant gases, R134a and R32, across various air dilution levels (0%, 5%, 10%, 25%, 50%, 70%). Each point in the plot represents a gas sample, with shapes indicating the level of air dilution within that type and colors showing the type of gas. Below we provide an analysis and some insights into the visualization:  
1. Distinct Clustering by Gas Type: The two gases, R134a and R32 are represented by red and green colors, respectively. The clusters formed by each gas type are distinct, demonstrating good class separability achieved by the t-SNE dimensionality reduction. This separability is crucial for the effectiveness of machine learning algorithms used in classifying these gases, as it indicates that the features extracted are robust enough to distinguish between the two gases reliably. 2. Influence of Dilution Levels: The variation in shapes within each color group illustrates the impact of different dilution levels on the feature space. It is evident that as the dilution level changes, there are shifts in the clustering patterns.