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METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z\u2019s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF\u2019s derived from a traditional SED template fitting method (Le Phare).<\/jats:p>","DOI":"10.1017\/s1743921317002186","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T07:04:51Z","timestamp":1496127891000},"page":"197-200","source":"Crossref","is-referenced-by-count":1,"title":["METAPHOR: Probability density estimation for machine learning based photometric redshifts"],"prefix":"10.1017","volume":"12","author":[{"given":"V.","family":"Amaro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3787-4196","authenticated-orcid":false,"given":"S.","family":"Cavuoti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Brescia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Vellucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Tortora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Longo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2017,5,30]]},"reference":[{"unstructured":"Sadeh I. , Abdalla F. 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