Description Usage Arguments Value Examples
View source: R/datasetReader.R
Read the file of the training and testing dataset, and perform preprocessing and data cleaning if necessary.
1 2 3 4 5 6 7 8 9 10 11 | datasetReader(
directory,
testDirectory,
selectedFeats = c(),
classCol = "class",
preProcessF = "N",
featuresToPreProcess = c(),
nComp = NA,
missingVal = c("NA", "?", " "),
missingOpr = 0
)
|
directory |
String of the directory to the file containing the training dataset. |
testDirectory |
String of the directory to the file containing the testing dataset. |
selectedFeats |
Vector of numbers of features columns to include from the training set and ignore the rest of columns - In case of empty vector, this means to include all features in the dataset file (default = c()). |
classCol |
String of the name of the class label column in the dataset (default = 'class'). |
preProcessF |
string containing the name of the preprocessing algorithm (default = 'N' –> no preprocessing):
|
featuresToPreProcess |
Vector of number of features to perform the feature preprocessing on - In case of empty vector, this means to include all features in the dataset file (default = c()) - This vector should be a subset of |
nComp |
Integer of Number of components needed if either "pca" or "ica" feature preprocessors are needed. |
missingVal |
Vector of strings representing the missing values in dataset (default: c('NA', '?', ' ')). |
missingOpr |
Boolean variable represents either delete instances with missing values or apply imputation using "MICE" library which helps you imputing missing values with plausible data values that are drawn from a distribution specifically designed for each missing datapoint- (default = 0 –> delete instances). |
List of the TrainingSet Train
and TestingSet Test
.
1 2 3 4 | ## Not run:
dataset <- datasetReader('/Datasets/irisTrain.csv', '/Datasets/irisTest.csv')
## End(Not run)
|
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