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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.
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Fig. 3 The strand partitioned training data structure.
To obtain a stable informational value, this partitioning, training, and testing was performed in terms of 500 different realizations.
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).
First, RPFM adopts the k-means method to partition the training set.
The proposed RPFM algorithm takes advantage of k-means method to partition the training set.
The algorithm employs a partition of training examples into several classes according to the average grey scale value of images.
Falcon-ART attempts to overcome the constraints faced by these two groups of fuzzy neural networks by using the fuzzy ART technique to partition the training data set.
To partition the training set R, we extract the context and the number of clusters according to the tree structure S and current level.
The proposed approach, called ensemble HMM (eHMM), attempts to partition the training data within the log-likelihood space and identify multiple clusters in an unsupervised manner.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com