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tokens: a very different architecture, PCA-Davies-nominal performs very similarly with scatter just below : . The increased sample size of the training sample, better purity, and improved intrinsic continuum recovery compared to PCA-Davies-smaller make a clear difference, as the latter has . Finally, using only wavelengths that would be observed in the optical at 5.5 z 6.2, PCA-optical only predictably performs worse than new PCAs and stacks of neighbours with access to longer wavelengths, but still better than both older PCAs and power-law reconstructions, with . Over the Ly β forest [972 λ(Å) 1000], the distinction in performance between methods is similar, with the Power-Law method doing the worst with at 1σ (2σ). Again, both larger-sample PCAs are more accurate with for PCA-Davies-smaller . The improved training sample improved the bias and reduced scatter below to for PCA-Davies-nominal , overtaking Neighbours stacking that has an accuracy of . PCANN-QSANNdRA performs very similarly to PCA-Davies-nominal , with an accuracy of . Finally, the optical wavelengths-only PCA performs significantly worse than IR PCAs but still better than power-law extrapolation, with . Beyond mean biases, the residuals from the different methods show very different wavelength dependences (Fig. 4 ). Residuals at the location of broad emission lines can be seen in the Power-Law, PCA-Pâris-10 , and PCA-Pâris-5 methods, while the Neighbours technique and PCA methods trained on larger samples ( PCA-Davies-smaller, PCA-Davies-nominal ) do not show residuals at the locations of the N ii 1085 Å, Fe iii 1125 Å, and C iii 1175 Å broad emission lines. This indicates that correlations between blue-side emission lines and red-side properties are, unsurprisingly, adequately captured by stacks of quasars with similar red-side properties. The more recent PCA decompositions also capture broad-line correlations. An important addition in the PCA-Davies-smaller and PCA-Davies-nominal was an independent fitting parameter |δ z | 0.05 to account for redshift uncertainties in the testing sample. Indeed, small residuals visible around the O vi line in PCANN-QSANNdRA are likely due to the lack of redshift correction as a fitting parameter, which is mitigated by the larger number of components (53/42 for the red/blue sides compared to 10/6). The absence of a redshift shift parameter in the other models might disadvantage them if the quasar redshifts in eBOSS are more inaccurate than in z > 5.5 quasars. Indeed, the systemic redshifts of high- z quasars are often known to be within |δ z | 0.0005 from detections of sub-millimetre emission lines in quasar hosts (Willott, Bergeron Omont 2015 ; Decarli et al. 2017 ; Eilers et al. 2020 ). The n > 5 components of the Pâris-10 PCA introduce shape perturbations at the edges of broad emission lines that account for some of these redshift uncertainties. We find that these high-order components give continua unphysical shapes (see e.g. Kakiichi et al. 2018