train_models: Train machine learning models on training data

In promor: Proteomics Data Analysis and Modeling Tools

Train machine learning models on training data

Description

This function can be used to train models on protein intensity data using different machine learning algorithms

Usage

train_models(
  split_df,
  resample_method = "repeatedcv",
  resample_iterations = 10,
  num_repeats = 3,
  algorithm_list,
  seed = NULL,
  ...
)

Arguments

split_df	A split_df object from performing split_data.
resample_method	The resampling method to use. Default is "repeatedcv" for repeated cross validation. See trainControl for details on other available methods.
resample_iterations	Number of resampling iterations. Default is 10.
num_repeats	The number of complete sets of folds to compute (For resampling method = "repeatedcv" only).
algorithm_list	A list of classification or regression algorithms to use. A full list of machine learning algorithms available through the caret package can be found here: http://topepo.github.io/caret/train-models-by-tag.html. See below for default options.
seed	Numerical. Random number seed. Default is NULL
...	Additional arguments to be passed on to train function in the caret package.

Details

• train_models function can be used to first define the control parameters to be used in training models, calculate resampling-based performance measures for models based on a given set of machine-learning algorithms, and output the best model for each algorithm.

• In the event that algorithm_list is not provided, a default list of four classification-based machine-learning algorithms will be used for building and training models. Default algorithm_list: "svmRadial", "rf", "glm", "xgbLinear, and "naive_bayes."

• Note: Models that fail to build are removed from the output.

• Make sure to fix the random number seed with seed for reproducibility

Value

A list of class train for each machine-learning algorithm. See train for more information on accessing different elements of this list.

Author(s)

Chathurani Ranathunge

References

Kuhn, Max. "Building predictive models in R using the caret package." Journal of statistical software 28 (2008): 1-26.

See Also

• pre_process

• trainControl

• train

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, seed = 8314)

## Fit models based on the default list of machine learning (ML) algorithms
covid_model_list1 <- train_models(split_df = covid_split_df, seed = 351)

## Fit models using a user-specified list of ML algorithms.
covid_model_list2 <- train_models(
  covid_split_df,
  algorithm_list = c("svmRadial", "glmboost"),
  seed = 351
)

## Change resampling method and resampling iterations.
covid_model_list3 <- train_models(
  covid_split_df,
  resample_method = "cv",
  resample_iterations = 50,
  seed = 351
)

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