tuneandtrainExtSVM {RobustPrediction}R Documentation

Tune and Train External SVM

Description

This function tunes and trains a Support Vector Machine (SVM) classifier using the mlr package. It provides two strategies for tuning the cost parameter based on the estperf argument:

Usage

tuneandtrainExtSVM(
  data,
  dataext,
  estperf = FALSE,
  kernel = "linear",
  cost_seq = 2^(-15:15),
  scale = FALSE
)

Arguments

data

A data frame containing the training data. The first column should be the response variable (factor), and the remaining columns should be the predictor variables.

dataext

A data frame containing the external validation data. The first column should be the response variable (factor), and the remaining columns should be the predictor variables.

estperf

A logical value indicating whether to use internal tuning with external validation (TRUE) or external tuning (FALSE). Default is FALSE.

kernel

A character string specifying the kernel type to be used in the SVM. Default is "linear".

cost_seq

A numeric vector specifying the sequence of cost values to evaluate. Default is 2^(-15:15).

scale

A logical value indicating whether to scale the predictor variables. Default is FALSE.

Value

A list containing the following components:

Examples


# Load sample data
data(sample_data_train)
data(sample_data_extern)

# Example usage with external tuning (default)
result <- tuneandtrainExtSVM(sample_data_train, sample_data_extern, kernel = "linear", 
  cost_seq = 2^(-15:15), scale = FALSE)
print(result$best_cost)        # Optimal cost
print(result$best_model)       # Final trained model
# Note: est_auc is not returned when estperf = FALSE

# Example usage with internal tuning and external validation
result_internal <- tuneandtrainExtSVM(sample_data_train, sample_data_extern, 
  estperf = TRUE, kernel = "linear", cost_seq = 2^(-15:15), scale = FALSE)
print(result_internal$best_cost)  # Optimal cost
print(result_internal$best_model) # Final trained model
print(result_internal$est_auc)    # AUC on external validation dataset


[Package RobustPrediction version 0.1.7 Index]