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* Abbreviations: EHR — : electronic health record PCA — : principal component analysis The field of machine learning involves the study of algorithms that can be used to discover mathematical functions for classification and prediction. Increasing computational power and larger data sets have allowed for the detection of complex, multidimensional associations not discoverable with traditional statistical modeling techniques, such as logistic regression. In this issue of Pediatrics , Ramgopal et al1 apply a mix of traditional statistical techniques and machine learning to the vexing problem of the young infant (≤60 days) with fever. Several clinical decision rules have been developed to help the clinician decide which patients are at low risk of bacterial infection and can forego lumbar puncture and/or hospital admission. The authors used a public use data set from the Pediatric Emergency Care Applied Research Network (PECARN) study of young infants with fever and tested several different models to attempt to improve on PECARN’s own decision rule.2 The authors were indeed able to improve on the PECARN decision rule, achieving an improvement in specificity from 60% to 75% without a loss of almost perfect sensitivity. They were able to accomplish this despite a small data set by machine learning standards, with only 138 patients with bacterial infection. The most successful model was a random forest … Address correspondence to James M. Chamberlain, MD, Division of Emergency Medicine, Children’s National Hospital, 111 Michigan Ave NW, Washington, DC 20010. E-mail: jchamber{at}childrensnational.org