trainClassifier: Train MetaClean Classifier

In KelseyChetnik/MetaClean: Detection of Low-Quality Peaks in Untargeted Metabolomics Data

Description

Wrapper function for training one of the 8 classification algorithms using one of the three available metrics sets.

Usage

trainClassifier(trainData, model, metricSet, hyperparameters)

Arguments

trainData	dataframe. Rows should correspond to peaks, columns should include peak quality metrics and class labels only.
model	Name of the classification algorithm to be trained from the eight available: DecisionTree, LogisiticRegression, NaiveBayes, RandomForest, SVM_Linear, AdaBoost, NeuralNetwork, and ModelAveragedNeuralNetwork.
metricSet	The metric set to be run with the selected model. Select from the following: M4, M7, and M11.
hyperparameters	dataframe of the tuned hyperparameters returned by runCrossValidation()

Value

a trained MetaClean model

Examples

# train classification algorithms
best_model <- trainClassifier(trainData=pqMetrics_development,
                                        model="AdaBoost",
                                        metricSet="M11",
                                        hyperparameters)

KelseyChetnik/MetaClean documentation built on May 17, 2021, 5:33 a.m.