A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest.
S. MuceshWilliam G. HartleyAntonella PalmeseOfer LahavL. WhitewayA. AmonKeith C. BechtolGary M. BernsteinAurelio Carnero RosellMatias Carrasco KindA. ChoiK. EckertS. EverettDaniel GruenRobert A. GruendlI. HarrisonE. M. HuffNikolay KuropatkinIgnacio Sevilla-NoarbeErin SheldonBrian YannyMichel AguenaSahar AllamD. BaconEmmanuel BertinS. BhargavaD. BrooksJorge CarreteroFrancisco J. CastanderChristopher J. ConseliceM. CostanziMartín CrocceLuiz Nicolaci da CostaM. E. S. PereiraJ. DeVicenteShantanu DesaiH. Thomas DiehlAlex Drlica-WagnerAugust E. EvrardI. FerreroBrenna L. FlaugherPablo FosalbaJoshua A. FriemanJuan García-BellidoEnrique GaztañagaDavid W. GerdesJulia GschwendG. GutierrezSamuel R. HintonDevon L. HollowoodKlaus HonscheidDavid J. JamesK. KuehnMarcos LimaH. LinMarcio A. G. MaiaPeter MelchiorFelipe MenanteauRamon MiquelRobert MorganFrancisco Paz-ChinchónAndreas Alejandro PlazasEusebio SánchezVictor E. ScarpineMichael S. SchubnellSantiago SerranoM. SmithEric SuchytaGregory G. TarléD. ThomasC. ToT. N. VargaR. D. WilkinsonPublished in: CoRR (2020)
Keyphrases
- random forest
- probability distribution
- random forests
- decision trees
- feature set
- ensemble methods
- decision tree learning algorithms
- fold cross validation
- feature importance
- ensemble classifier
- feature selection
- bayesian networks
- ensemble learning
- machine learning methods
- rotation forest
- data mining
- multi label
- prediction accuracy
- semi supervised
- machine learning