extract_confusion_matrix: Internal function to extract the confusion matrix.

In familiar: End-to-End Automated Machine Learning and Model Evaluation

Internal function to extract the confusion matrix.

Description

Computes and extracts the confusion matrix for predicted and observed categorical outcomes used in a familiarEnsemble object.

Usage

extract_confusion_matrix(
  object,
  data,
  cl = NULL,
  ensemble_method = waiver(),
  detail_level = waiver(),
  is_pre_processed = FALSE,
  message_indent = 0L,
  verbose = FALSE,
  ...
)

Arguments

object	A familiarEnsemble object, which is an ensemble of one or more familiarModel objects.
data	A dataObject object, data.table or data.frame that constitutes the data that are assessed.
cl	Cluster created using the parallel package. This cluster is then used to speed up computation through parallellisation.
ensemble_method	Method for ensembling predictions from models for the same sample. Available methods are: median (default): Use the median of the predicted values as the ensemble value for a sample. mean: Use the mean of the predicted values as the ensemble value for a sample.
detail_level	(optional) Sets the level at which results are computed and aggregated. ensemble: Results are computed at the ensemble level, i.e. over all models in the ensemble. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the model performance of the ensemble model for each bootstrap. hybrid (default): Results are computed at the level of models in an ensemble. This means that, for example, bias-corrected estimates of model performance are directly computed using the models in the ensemble. If there are at least 20 trained models in the ensemble, performance is computed for each model, in contrast to ensemble where performance is computed for the ensemble of models. If there are less than 20 trained models in the ensemble, bootstraps are created so that at least 20 point estimates can be made. model: Results are computed at the model level. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the performance of the model for each bootstrap. Note that each level of detail has a different interpretation for bootstrap confidence intervals. For ensemble and model these are the confidence intervals for the ensemble and an individual model, respectively. That is, the confidence interval describes the range where an estimate produced by a respective ensemble or model trained on a repeat of the experiment may be found with the probability of the confidence level. For hybrid, it represents the range where any single model trained on a repeat of the experiment may be found with the probability of the confidence level. By definition, confidence intervals obtained using hybrid are at least as wide as those for ensemble. hybrid offers the correct interpretation if the goal of the analysis is to assess the result of a single, unspecified, model. hybrid is generally computationally less expensive then ensemble, which in turn is somewhat less expensive than model. A non-default detail_level parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"="ensemble", "model_performance"="hybrid"). This parameter can be set for the following data elements: auc_data, decision_curve_analyis, model_performance, permutation_vimp, ice_data, prediction_data and confusion_matrix.
is_pre_processed	Flag that indicates whether the data was already pre-processed externally, e.g. normalised and clustered. Only used if the data argument is a data.table or data.frame.
message_indent	Number of indentation steps for messages shown during computation and extraction of various data elements.
verbose	Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.
...	Unused arguments.

Value

A data.table containing predicted and observed outcome data together with a co-occurence count.

familiar documentation built on Sept. 30, 2024, 9:18 a.m.