healthcareai-r
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HealthCatalyst
R tools for healthcare machine learning
Say you train a model on a subset of the data, or for whatever reason you burned a lot of cycles training a model and then you want a new...
This awesome function takes `nominal` or `numeric` params. Ordinal type cols fall between `nominal` and `numeric` and it isn't clear to the user which to use (since behavior changes from...
machine_learn function uses the prep_data function to prepare the data which is feed to the tune_model or flash model later. prep_data function have !!!dots or ignored variable which should not...
machine_learn function uses tune_models or flash_models function for model training but it does not use the ignored variable in model training. these ignored variable may have some vital information in...
It seems like leaving the outcome in its original form is the desired behavior, but if we want to do things with predicted classes (e.g. choosing thresholds) we likely want...
There is currently a user-customizable parameter that controls how to weight cohesion vs. ubiquity for classification (`cohesion_weight`). It currently doesn't do anything for regression. But, there is a exponent on...
From a quick check, it looks like n_levels and outcome are silently ignored, groups and id are required, and arguments passed to `pivot` are honored. The `pivot` thing is dangerous,...
Now that we've got the `all_models` flag on `evaluate`, we could have a plot method that plots a bar or dot plot for each model's performance at its best hyperparameter...
Like `missingness`. Gist is: ``` d %>% summarize_if(~ is.character(.x) | is.factor(.x) , n_distinct) ```