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Using random forest based outlier detection and imputation to clean a #kaggle dataset. (Specifically the outForest and isotree #rstats packages 
, which implement different kinds of random forest methods.)
Achieved only a marginal gain in out-of-sample accuracy, but the real treasure was the methods I learned along the way. 
tylerburleigh.comUsing random forest based outlier detection to clean a training dataset | Tyler BurleighIn this post, I explore whether a random forest model can be improved by using random forest based multivariate outlier detection and imputation methods, and by reducing feature multicollinearity. Supporting the common wisdom that random forest models are robust to outliers and multicollinearity, these data cleaning steps led to only marginal improvements in out-of-sample model performance.