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tokens: correct. A more detailed analysis of the disagreements between the classifiers (including the visual inspection) will be provided in a follow-up paper. However, we note that this superficial comparison between the classifiers should be enough to establish a classification strategy for new surveys. We recommend that all the available classifiers are used in a combined way. Because the different classifiers have completely different approaches to the classification problem, their agreement will reinforce the confidence of the classification. We currently recommend that all automated analysis are applied to a representative sample that is visually inspected in order to develop a decision tree. 7 SUMMARY AND CONCLUSIONS We have presented squeze , an algorithm that follows the human visual inspection to classify quasars and estimate their redshift. We have then tested the performance of this algorithm on visually inspected data from BOSS. We now summarize the key points we have discussed so far. squeze mimics the procedure of human visual inspection of the spectra to identify quasars and estimate their redshift. It performs a three step classification of quasars by identifying emission peaks, computing the redshift assuming an emission line, and selecting the correct quasar redshifts from the different possible values of z try . If we assume a confidence threshold of 0.32, we recover a purity of and a completeness of for the entire sample. This purity and completeness increases to 99.59 ± 0.06 and to , respectively, if we restrict to objects with z try ≥ 2.1. We can achieve virtually 100 per cent purity, if we assume a higher confidence threshold. For instance, for p min = 0.7, we recover a purity of and a completeness of for objects with z try ≥ 2.1. The analysis of a more realistic quasar redshift distribution produced by combining plates from BOSS and SEQUELS suggests that our results are slightly optimistic: for this analysis, and considering p min = 0.32, yields a purity and completeness of and , respectively. We find the standard deviation of our redshift errors to be 1500 km s −1 . We recommend the use of squeze redshift estimates as a prior to some additional redshift tuning. We discuss options to reduce these errors, but further study is required. Preliminary analysis suggests that the errors could be reduced to less than 1200 km s −1 . The performance of squeze presented here can be regarded as a conservative assessment since it is assumed that, where squeze disagrees with visual inspection, visual inspection is always correct. We will assess this in future papers in this series. We will also explore the versatility of squeze and its applicability to survey data that differs significantly from the BOSS spectra used here. APPENDIX A: OPTIMIZATION OF STEP 1 In Section 2.1 , we have explained squeze three-steps algorithm in detail. In particular, we have explained that the identification of emission peaks is done using a native peak finder.