DaMiR.EnsembleLearning2cl_Training {DaMiRseq} | R Documentation |
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.
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"))
data |
A transposed data frame of normalized expression data. Rows and Cols should be, respectively, observations and features |
classes |
A class vector with |
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 |
This function implements the training step of DaMiR.EnsembleLearning2cl function
A list containing:
The models of each classifier used to build the Ensemble meta-learner with the median or the best accuracy (over the iteration) for the Ensemble classifier;
the weights associated to each weak classifier;
Mattia Chiesa, Luca Piacentini
# 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"))