mikropml (version 1.6.1)

define_cv: Define cross-validation scheme and training parameters

Description

Define cross-validation scheme and training parameters

Usage

define_cv(
  train_data,
  outcome_colname,
  hyperparams_list,
  perf_metric_function,
  class_probs,
  kfold = 5,
  cv_times = 100,
  groups = NULL,
  group_partitions = NULL
)

Value

Caret object for trainControl that controls cross-validation

Arguments

train_data

Dataframe for training model.

outcome_colname

Column name as a string of the outcome variable (default NULL; the first column will be chosen automatically).

hyperparams_list

Named list of lists of hyperparameters.

perf_metric_function

Function to calculate the performance metric to be used for cross-validation and test performance. Some functions are provided by caret (see caret::defaultSummary()). Defaults: binary classification = twoClassSummary, multi-class classification = multiClassSummary, regression = defaultSummary.

class_probs

Whether to use class probabilities (TRUE for categorical outcomes, FALSE for numeric outcomes).

kfold

Fold number for k-fold cross-validation (default: 5).

cv_times

Number of cross-validation partitions to create (default: 100).

groups

Vector of groups to keep together when splitting the data into train and test sets. If the number of groups in the training set is larger than kfold, the groups will also be kept together for cross-validation. Length matches the number of rows in the dataset (default: NULL).

group_partitions

Specify how to assign groups to the training and testing partitions (default: NULL). If groups specifies that some samples belong to group "A" and some belong to group "B", then setting group_partitions = list(train = c("A", "B"), test = c("B")) will result in all samples from group "A" being placed in the training set, some samples from "B" also in the training set, and the remaining samples from "B" in the testing set. The partition sizes will be as close to training_frac as possible. If the number of groups in the training set is larger than kfold, the groups will also be kept together for cross-validation.

Author

Begüm Topçuoğlu,

Kelly Sovacool,

Examples

training_inds <- get_partition_indices(otu_small %>% dplyr::pull("dx"),
  training_frac = 0.8,
  groups = NULL
)
train_data <- otu_small[training_inds, ]
test_data <- otu_small[-training_inds, ]
cv <- define_cv(train_data,
  outcome_colname = "dx",
  hyperparams_list = get_hyperparams_list(otu_small, "glmnet"),
  perf_metric_function = caret::multiClassSummary,
  class_probs = TRUE,
  kfold = 5
)