Super Learner

Laan, Mark J. van der; Polley, Eric C.; Hubbard, Alan

doi:10.2202/1544-6115.1309

Cited by 1,489 publications

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“…We have provided a simple explanation of the Super Learner to facilitate a more widespread use in epidemiology. More advanced treatments with realistic data examples are available 5,7,31 …”

Section: Discussionmentioning

confidence: 99%

“…2 More recently, van der Laan and colleagues proved that stacking possesses certain ideal theoretical properties. [3][4][5] In particular, their oracle inequality guarantees that in large samples the algorithm will perform at least as well as the best individual predictor included in the ensemble.…”

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confidence: 99%

“…Combining theŶ gam-cv andŶ earth-cv under these constraints (non-negative estimates that sum to 1) is referred to as a "convex combination," and is motivated by both theoretical results and improved stability in practice. 2,5 Non-negative least squares corresponds to minimizing the mean squared error, which is our chosen loss function (and thus, fulfills our objective). We then normalize the coefficients from this regression to sum to 1.…”

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confidence: 99%

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Stacked Generalization: An Introduction to Super Learning

Naimi

Balzer

2017

Preprint

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Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into what is now known as “Super Learner”. Super Learner uses V -fold cross-validation to build the optimal weighted combination of predictions from a library of candidate algorithms. Optimality is defined by a user-specified objective function, such as minimizing mean squared error or maximizing the area under the receiver operating characteristic curve. Although relatively simple in nature, use of the Super Learner by epidemiologists has been hampered by limitations in understanding conceptual and technical details. We work step-by-step through two examples to illustrate concepts and address common concerns.

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“…We have provided a simple explanation of the Super Learner to facilitate a more widespread use in epidemiology. More advanced treatments with realistic data examples are available 5,7,31 …”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Stacked Generalization: An Introduction to Super Learning

Naimi

Balzer

2017

Preprint

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“…The Super Learner has been proposed as a method for selecting via cross-validation the optimal regression algorithm among all weighted combinations of a set of given candidate algorithms, henceforth referred to as the library [21,27,28] ( Fig. 20.1).…”

Section: Prediction Algorithmsmentioning

confidence: 99%

“…Subsequently, the prediction rule consisting of the CV-MSE-minimizing weighted convex combination of all candidate algorithms was also computed and refitted on all data. This is what we refer to as the Super Learner combination algorithm [28].…”

Section: Prediction Algorithmsmentioning

confidence: 99%

Mortality Prediction in the ICU Based on MIMIC-II Results from the Super ICU Learner Algorithm (SICULA) Project

Pirracchio

2016

Secondary Analysis of Electronic Health Records

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Learning ObjectivesIn this chapter, we illustrate the use of MIMIC II clinical data, non-parametric prediction algorithm, ensemble machine learning, and the Super Learner algorithm. IntroductionPredicting mortality in patients hospitalized in intensive care units (ICU) is crucial for assessing severity of illness and adjudicating the value of novel treatments, interventions and health care policies. Several severity scores have been developed with the objective of predicting hospital mortality from baseline patient characteristics, defined as measurements obtained within the first 24 h after ICU admission. The first scores proposed, APACHE [1] (Acute Physiology and Chronic Health Evaluation), APACHE II [2], and SAPS [3] (Simplified Acute Physiology Score), relied upon subjective methods for variable importance measure, namely by prompting a panel of experts to select and assign weights to variables according to perceived relevance for mortality prediction. Further scores, such as the SAPS II [4] were subsequently developed using statistical modeling techniques [4][5][6][7]. To this day, the SAPS II [4] and APACHE II [2] scores remain the most widely used in clinical practice. However, since first being published, they have been modified several times in order to improve their predictive performance [6][7][8][9][10][11]. Despite these extensions of SAPS, predicted hospital mortality remains generally overestimated [8,9,[12][13][14]. As an illustration, Poole et al. [9] compared the SAPS II and the SAPS3 performance in a cohort of more than 28,000 admissions to 10 different Italian ICUs. They concluded that both scores provided unreliable predictions, but unexpectedly the newer SAPS 3 turned out to overpredict mortality more than the

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