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To obtain a stable informational value, this partitioning, training, and testing was performed in terms of 500 different realizations.
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Fig. 3 The strand partitioned training data structure.
A decision tree can be treated as a set of Boolean functions of features and these conjunctions of features partition training samples into groups with homogenous class label.
The Kappa statistics is computed from the 70 patients' records via a 10-fold cross-validation with each fold of different combination of partitions (training and testing) as different inter-observers, randomly picked from the whole dataset.
This partition-training-and-validation procedure is repeated for 20 times, and the performance of the model (in terms of specificity, sensitivity, accuracy and correlation coefficient) is averaged over the 200 validation folds (10 validation folds per partition ×20 partitions).
The data partitioning as training set and test had been done to avoid over training and over parameterization.
Figure 9 Partitioning into training and test set: A regression dataset is split into a training and a test set which is performed by the Split Dataset Into Train-/Testset.
The data partitioning as training and test set was done to avoid over-training and over parameterization.
Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine.
Extensive analysis is conducted in order to assess the significance of the features employed, the partitioning of training data and the complexity of the architecture.
In the worst case, overall accuracy varied between ~40 80% in one resampling design, due only to random variation in partitioning into training and test sets.
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