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tokens: the lowest error but with the longest training time, whereas the ERT can be seen to reach a very similar error much faster. 4 RESULTS The results given in Table 2 show how the seven ML algorithms performed at producing photometric redshift estimations. Furthermore, Fig. 6 displays the true spectroscopic redshifts plotted against the photometric redshift estimates for each ML algorithm. We also plotted the distributions of the redshift estimations for each of the algorithms as well as the true spectroscopic redshift in a violin plot in Fig. 7 to quickly see which algorithms were able to capture the correct distribution. Figure 6. Graphs of photometric redshift estimates against the true spectroscopic redshift where the lighter shaded contours display the more densely populated regions. From top left to bottom right – Linear Regression (LR), k -Nearest Neighbours (kNN), Multilayer Perceptron (MLP), Decision Tree (DT), Boosted Decision Tree (BDT), Random Forest (RF), and Extremely Randomized Trees (ERT). Figure 7. Violin plots showing the kernel density estimation of the underlying distributions of photometric redshift estimates of each algorithm along with the true spectroscopic redshift. From left to right – True spectroscopic redshift (zspec), Linear Regression (LR), k -Nearest Neighbours (kNN), Decision Tree (DT), Boosted Decision Tree (BDT), Random Forest (RF), Extremely Randomized Trees (ERT), and Multilayer Perceptron (MLP). From these results, we saw that all algorithms were able to successfully provide photometric redshift estimations. Using the violin plots from Fig. 7 , we could see that the rough distribution was recovered by each algorithm, with the Multilayer Perceptron (MLP) producing a slightly more similar shape to the true redshifts. However, from simply looking at the outputs shown in Figs 6 and 7 , it would be very difficult to determine which algorithm would be best to use. While the Decision Tree (DT) might be excluded due to the estimates being put into bands at set redshifts, its errors were still found to be quite low and it outperformed both the Linear Regression (LR) and MLP algorithms. Looking at the metrics in Table 2 alone, the Random Forest (RF) performed best having the lowest errors with an MAE = 0.0385 and MSE = 0.0042; however, the other algorithms k -Nearest Neighbours (kNN), Boosted Decision Tree (BDT), and Extremely Randomized Trees (ERT) all performed incredibly similarly with MAE 0.042 and MSE 0.0046. Indeed, the BDT was almost identically performing to the RF with a slightly improved precision and outlier fraction, and with such close performances of all the other algorithms, it was impossible to sufficiently determine which algorithm would be the most useful. To be able to further differentiate between them and determine which would be the best algorithm to use, it was therefore necessary to use the benchmarking results. The results of the benchmarking performed for each algorithm are plotted in Fig. 1