healthcareai-r
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HealthCatalyst
R tools for healthcare machine learning
- This could identify numeric and low-cardinality categoricals and calculate correlations between them - `plot.` method could call `corrplot` or `ggpairs`
From ?train: "If custom performance metrics are used (via the summaryFunction argument in trainControl, the value of metric should match one of the arguments..." - [ ] accuracy and kappa?...
[Create interactions](https://topepo.github.io/recipes/reference/step_interact.html) including polynomial terms. This shouldn't be done for tree-based models that can get at interactions other ways, but it can be very useful for regression based models. Consider...
Seems like it would be at least nice, and maybe essential, to implement this as part of the recipes pipeline so that whatever's done to training data can be applied...
relates to https://github.com/HealthCatalyst/healthcareai-py/issues/369 https://github.com/mkleehammer/pyodbc/wiki/Connecting-to-SQL-Server-from-Mac-OSX
I currently do this hackily inside the train lapply in tune_models. Let's us modify default random hyperparameter search parameters, customize hyperparameters, etc.
`seed` argument to `tune_models` with no default. `if (!missing(seed)) set.seed(seed); train...`.
classification. It's a good one to have for high cardinality features.