test_models: Test machine learning models on test data

In promor: Proteomics Data Analysis and Modeling Tools

Test machine learning models on test data

Description

This function can be used to predict test data using models generated by different machine learning algorithms

Usage

test_models(
  model_list,
  split_df,
  type = "prob",
  save_confusionmatrix = FALSE,
  file_path = NULL,
  ...
)

Arguments

model_list	A model_list object from performing train_models.
split_df	A split_df object from performing split_data.
type	Type of output. Set type as "prob" (default) to output class probabilities, and "raw" to output class predictions.
save_confusionmatrix	Logical. If TRUE, a tab-delimited text file ("Confusion_matrices.txt") with confusion matrices in the long-form data format will be saved in the directory specified by file_path. See below for more details.
file_path	A string containing the directory path to save the file.
...	Additional arguments to be passed on to predict.

Details

• test_models function uses models obtained from train_models to predict a given test data set.

• Setting type = "raw" is required to obtain confusion matrices.

• Setting type = "prob" (default) will output a list of probabilities that can be used to generate ROC curves using roc_plot.

Value

• probability_list: If type = "prob", a list of data frames containing class probabilities for each method in the model_list will be returned.

• prediction_list: If type = "raw", a list of factors containing class predictions for each method will be returned.

Author(s)

Chathurani Ranathunge

See Also

• split_df

• train_models

• predict

• confusionMatrix

Examples

## Create a model_df object
covid_model_df <- pre_process(covid_fit_df, covid_norm_df)

## Split the data frame into training and test data sets
covid_split_df <- split_data(covid_model_df)

## Fit models using the default list of machine learning (ML) algorithms
covid_model_list <- train_models(covid_split_df)

# Test a list of models on a test data set and output class probabilities,
covid_prob_list <- test_models(model_list = covid_model_list, split_df = covid_split_df)


## Not run: 
# Save confusion matrices in the working directory and output class predictions
covid_pred_list <- test_models(
  model_list = covid_model_list,
  split_df = covid_split_df,
  type = "raw",
  save_confusionmatrix = TRUE,
  file_path = "."
)

## End(Not run)

promor documentation built on July 26, 2023, 5:39 p.m.