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GINI index is used for splitting the data.
Splitting the data into two (binary) subsets comprises the first stage of the process.
Given that we will create a data-parallel design, there are several axes along which we considered splitting the data.
The cross-validation has been achieved by splitting the data into five blocks preserving the initial class distribution.
In addition, to check the statistical accuracy of MODPEP, we have repeated the validating procedure by splitting the data set into training and test sets for 10 runs.
Decision tree is built through an iterative process of splitting the data into partitions, and then splitting up further on each of the branches.
As other MapReduce frameworks, the execution begins by splitting the data inputs into chunks and sending them to the Map phase.
Rather than perform this type of learning, we consider splitting the data to train expert network first followed by gating network and continue learning after gating is trained.
We train our network by splitting the data into square regions and use a pooling layer that respects the permutation-invariance of the input points.
This associated process of splitting the data for training and evaluating the learning is known as cross validation.
Briefly, cross validating a model is done by first splitting the data set into a number of equally sized partitions.
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