"data splitting" is a correct and usable phrase in written English.
You can use it when describing the process of breaking up a set of data into smaller, more manageable parts. For example, "The data engineer used data splitting to create smaller subsets of customer survey data for more targeted analysis."
The over-fitting bias can be eliminated through data splitting at the cost of statistical efficiency, and I will propose a repeated data splitting approach to mitigate the efficiency loss.
Moreover, Kennard Stone data splitting algorithm contributed to significant model performance enhancement.
Subsequently, for model development, the advantage of algorithm based data splitting over random selection is presented.
Compared with data splitting, nested cross-validation results in better models and more stable estimates of performance, since every subject contributes to the performance estimates.
In this work, we apply three different data splitting strategies and propose alternative values for the embedded PSSM parameters.
The basic principle of data splitting is to choose train data that is able to represent the whole data.
The data splitting process is repeated many times and the cross-validated error estimates are averaged over all data splits.
We parallelized JD by data-splitting techniques, combining them with techniques to reduce the amount of communication data.
The land-use model performed well classifying correctly 85.9% of cases validated using a data-splitting strategy.
The procedure is known as test set method, hold-out-method, or (one-time) data-splitting [25],[25].
Rather than splitting data into test and validation sets, we performed a cross-validation, that uses repeated data-splitting to prevent model overfitting and to generate accurate estimates of model coefficients, being a compelling statistical technique for model validation [14].
