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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