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    1. POWhich classification packages in R have a non-formula interface (e.g., f(x,y) instead of f(y ~ ., data))?
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    2. POUsing mclapply, foreach, or something else in [r] to operate on an object in parallel?
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    3. POGradient boosting predictions in low-latency production environments?
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      1. POHow to make a great R reproducible example
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      1. POWhen should I use the := operator in data.table?
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      1. POHow to load big csv file with mixed-type columns using the bigmemory package
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    1. COI had tried to find information about scikit-learn prediction times in July but couldn't, so I chose to use shallow learning algorithms instead (that I knew would meet the latency requirements). My use case was ranking bids in online advertising, so batch prediction would've helped, but decisioning for each impression still had to complete in less than 10ms. Re: your suggestions to reduce the # of trees, I tried 1, just read through 2, and will look into 3 -- thanks for the references/ideas. For a new project, I am considering Weka and scikit-learn, so your response was helpful.
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    2. CONo one seems to have mentioned this minor detail, but all of your clients who signed up 13 months ago will be categorized as '3+ years' unless you change the `> 13` to `>= 13` ;)
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      1. POProperly building a function in R
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    3. COYou should probably be using "family id" for the random effect part of the formula, if you're trying to estimate a separate variance component for families. This would make sense if you expected your response to be correlated within families, for example (which it probably would be). You could (additionally) include an individual-level random effect possibly correlated with the family-level random effect IF you had multiple observations per individual. It does not appear you do. I would reformat your question and ask on stats.stackexchange.com if your question is more statistical in nature.
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      1. POTo specify Family id as random effect in GLMM or lme
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