Machine Learning for Photometric Redshift Estimation of Quasars with Different Samples

We compare the performance of Support Vector Machine, XGBoost, LightGBM, k-Nearest Neighbors, Random forests and Extra-Trees on the photometric redshift estimation of quasars based on the SDSS_WISE sample. For this sample, LightGBM shows its superiority in speed while k-Nearest Neighbors, Random forests and Extra-Trees show better performance. Then k-Nearest Neighbors, Random forests and Extra-Trees are applied on the SDSS, SDSS_WISE, SDSS_UKIDSS, WISE_UKIDSS and SDSS_WISE_UKIDSS samples. The results show that the performance of an algorithm depends on the sample selection, sample size, input pattern and information from different bands; for the same sample, the more information the better performance is obtained, but different algorithms shows different accuracy; no single algorithm shows its superiority on every sample.