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Cross-validation [57] was implemented using a meta-node in KNIME that divides training dataset via stratified sampling.
The GARP program further divides training data into intrinsic training and extrinsic testing subsets (80% and 20% respectively herein) prior to each model building process [78].
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Data remaining after extraction of the external validation set was further divided into a training pool (further divided into "training" and "verification" sets) and an external test set.
In this experiment, we divided training classes into five portions of 20%%.
In this experiment, we divided training samples of each class into five portions of 20%%.
Divide training examples into two sets, a training set (95%%) and a validation set (5%%); Predict the class labels for the validation set by using the examples in the training set; and.
Welch divided training content into three areas, knowledge, awareness, and skills [ 35].
Additional file 1: The predictive performance of the SMO method on 30 randomly divided training and independent test datasets.
The data were divided into training (60%) validation (20%) and testing (20%) sets.
A total of 17,500 samples are collected and divided in training (90%) and validation (10%) sets.
The data are divided into training (60% of data), validation (20% of data), and test sets (20% of data).
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