Assessing performance of prediction rules in machine learning

Pharmacogenomics

Volumn 7, Issue 4, 2006, Pages 543-550

  Martin, Rory ^a   Yu, Kai ^b,c  

^a MILLENNIUM PHARMACEUTICALS INC   (United States)

^b WASHINGTON UNIVERSITY   (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Bootstrap; Machine learning; Monte Carlo simulation; Prediction rule; Split sample; Stochastic gradient boosting; True error

Indexed keywords

ANTINEOPLASTIC AGENT; ANTITHROMBOCYTIC AGENT; PROTEASOME; PROTEASOME INHIBITOR;

ACCURACY; ACUTE CORONARY SYNDROME; ALGORITHM; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; DRUG DESIGN; GENOMICS; INFORMATION SYSTEM; INTERMETHOD COMPARISON; LEARNING; MEDICAL DECISION MAKING; MONTE CARLO METHOD; PRACTICE GUIDELINE; PREDICTION; RANDOMIZATION; RELIABILITY; STATISTICAL ANALYSIS;

ALGORITHMS; ANTINEOPLASTIC AGENTS; ARTIFICIAL INTELLIGENCE; CORONARY DISEASE; HUMANS; MONTE CARLO METHOD; OLIGOPEPTIDES; PEPTIDE LIBRARY; PHARMACOGENETICS; PLATELET AGGREGATION INHIBITORS; PROTEASE INHIBITORS; PROTEASOME ENDOPEPTIDASE COMPLEX;

EID: 33745147888     PISSN: 14622416     EISSN: 17448042     Source Type: Journal    
DOI: 10.2217/14622416.7.4.543     Document Type: Article

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