DaMiR.EnsembleLearning2cl_Training {DaMiRseq}R Documentation

Train a Binary Classifier using 'Staking' Learning strategy.

Description

This function learn a meta learner by a 'Stacking' strategy. Users can provide heterogeneous features (other than genomic features) which will be taken into account during classification model building. A 'two-classes' classification task is addressed.

Usage

DaMiR.EnsembleLearning2cl_Training(data, classes, variables,
  fSample.tr.w = 0.7, iter = 100, cl_type = c("RF", "SVM", "LDA", "LR",
  "NB", "NN", "PLS"), type_model = c("median", "best"))

Arguments

data

A transposed data frame of normalized expression data. Rows and Cols should be, respectively, observations and features

classes

A class vector with nrow(data) elements. Each element represents the class label for each observation. Two different class labels are allowed

variables

An optional data frame containing other variables (but without 'class' column). Each column represents a different covariate to be considered in the model

fSample.tr.w

Fraction of samples of training set to be used during weight estimation; default is 0.7

iter

Number of iterations to assess classification accuracy; default is 100

cl_type

List of weak classifiers that will compose the meta-learners. "RF", "SVM", "LDA", "LR", "NB", "NN", "PLS" are allowed. Default is c("RF", "LR", "LDA", "NB", "SVM")

type_model

Select the model with the median or best accuracy over the iteration. "median" and "best" are allowed. Default: median

Details

This function implements the training step of DaMiR.EnsembleLearning2cl function

Value

A list containing:

Author(s)

Mattia Chiesa, Luca Piacentini

Examples

# use example data:
data(selected_features)
data(df)
set.seed(1)
# For the example:
# speed up the process setting a low 'iter' argument value;
# for real data set use default 'iter' value (i.e. 100) or higher:
#  Classification_res <- DaMiR.EnsembleLearning2cl_Training(
#  selected_features,classes=df$class, fSample.tr.w=0.6, iter=3,
# cl_type=c("RF","LR"))


[Package DaMiRseq version 1.8.0 Index]