A named list of package-supplied models can be obtained interactively with the r rdoc_url("modelinfo()") function, and includes a descriptive "label" for each, source "packages" on which the models depend, supported response variable "types", and "arguments" that can be specified in calls to the model functions. Function modelinfo can be called without arguments, with one or more model functions, variable types, or response variables; and will return information on all models matching the calling arguments.
## Analysis libraries library(MachineShop) library(magrittr) ## All availble models modelinfo() %>% names ## Model-specific information modelinfo(C50Model, CoxModel)
## All factor response-specific models modelinfo(factor(0)) %>% names ## Identify factor response-specific models modelinfo(factor(0), AdaBagModel, C50Model, CoxModel) %>% names
## Models for a responses variable modelinfo(iris$Species) %>% names
A named list of supplied metrics can be obtained with the r rdoc_url("metricinfo()") function, and includes a descriptive "label" for each, whether to "maximize" the metrics for better performance, their function "arguments", and supported observed and predicted response variable "types". Function r rdoc_url("metricinfo()") may be called without arguments, with one or more metric functions, an observed response variable, an observed and predicted response variable pair, response variable types, or resampled output; and will return information on all matching metrics.
## Analysis libraries library(MachineShop) library(magrittr) ## All availble metrics metricinfo() %>% names ## Metric-specific information metricinfo(auc, r2)
## Metrics for observed and predicted response variable types metricinfo(factor(0)) %>% names metricinfo(factor(0), factor(0)) %>% names metricinfo(factor(0), matrix(0)) %>% names ## Identify factor-specific metrics metricinfo(factor(0), accuracy, auc, r2) %>% names
## Metrics for observed and predicted responses from a model fit model_fit <- fit(Species ~ ., data = iris, model = C50Model) obs <- response(model_fit) pred <- predict(model_fit, type = "prob") metricinfo(obs, pred) %>% names
## Metrics for resampled output model_res <- resample(Species ~ ., data = iris, model = C50Model) metricinfo(model_res) %>% names
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