  Statistical learning (SL) includes methods that extract knowledge from
complex data. SL methods beyond generalized linear models are being
increasingly implemented in public health research and epidemiology because
they can perform better in instances with complex or high-dimensional
data---settings when traditional statistical methods fail. These novel methods,
however, often include random sampling which may induce variability in results.
Best practices in data science can help to ensure robustness. As a case study,
we included four SL models that have been applied previously to analyze the
relationship between environmental mixtures and health outcomes. We ran each
model across 100 initializing values for random number generation, or "seeds,"
and assessed variability in resulting estimation and inference. All methods
exhibited some seed-dependent variability in results. The degree of variability
differed across methods and exposure of interest. Any SL method reliant on a
random seed will exhibit some degree of seed sensitivity. We recommend that
researchers repeat their analysis with various seeds as a sensitivity analysis
when implementing these methods to enhance interpretability and robustness of
results.

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