These methods tidy the variable importance of a random forest model summary,
augment the original data with information on the fitted
values/classifications and error, and construct a one-row glance of the
model's statistics.

# S3 method for randomForest
augment(x, data = NULL, ...)
# S3 method for randomForest
glance(x, ...)
# S3 method for randomForest
tidy(x, ...)

## Arguments

x |
randomForest object |

data |
Model data for use by `augment.randomForest()` . |

... |
Additional arguments (ignored) |

## Value

`augment.randomForest`

returns the original data with additional columns:

.oob_timesThe number of trees for which the given case was "out of bag". See `randomForest::randomForest()`

for more details.

.fittedThe fitted value or class.

augment returns additional columns for classification and usupervised trees:

.votesFor each case, the voting results, with one column per class.

.local_var_impThe casewise variable importance, stored as data frames in a nested list-column, with one row per variable in the model. Only present if the model was created with `importance = TRUE`

glance.randomForest returns a data.frame with the following
columns for regression trees:

mseThe average mean squared error across all trees.

rsqThe average pesudo-R-squared across all trees. See `randomForest::randomForest()`

for more information.

For classification trees: one row per class, with the following columns:

precision
recall
accuracy
f_measure
All tidying methods return a data.frame without rownames. The
structure depends on the method chosen.
tidy.randomForest returns one row for each model term, with the following columns:

termThe term in the randomForest model

MeanDecreaseAccuracyA measure of variable importance. See `randomForest::randomForest()`

for more information. Only present if the model was created with `importance = TRUE`

MeanDecreaseGiniA measure of variable importance. See `randomForest::randomForest()`

for more information.

MeanDecreaseAccuracy_sdStandard deviation of `MeanDecreaseAccuracy`

. See `randomForest::randomForest()`

for more information. Only present if the model was created with `importance = TRUE`

classwise_importanceClasswise variable importance for each term, stored as data frames in a nested list-column, with one row per class. Only present if the model was created with `importance = TRUE`