In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training. A common method to avoid over-training is the hold-out crossvalidation. The basic problem of this method represents, however, appropriate data...

background & aim: in many medical studies, one data set is used to construct the model, and to test its performance. this approach is prone to over optimization, and leads to statistics with low chance of external validity. data splitting can be used to create training and test sets but the cost is reduction in power. the aim of this study was to demonstrate the ability of bootstrap aggregating...

The present work introduces a modified scheme for the solution of compressible 2-D full Navier-Stokes equations, using Flux Vector Splitting method. As a result of this modification, numerical diffusion is reduced. The computer code which is developed based on this algorithm can be used easily and accurately to analyze complex flow fields with discontinuity in properties, in cases such as shock...

It is common to split a dataset into training and testing sets before fitting statistical or machine learning model. However, there no clear guidance on how much data should be used for testing. In this article we show that the optimal splitting ratio $\sqrt{p}:1$, where $p$ number of parameters in linear regression model explains well.

The present work introduces a modified scheme for the solution of compressible 2-D full Navier-Stokes equations, using Flux Vector Splitting method. As a result of this modification, numerical diffusion is reduced. The computer code which is developed based on this algorithm can be used easily and accurately to analyze complex flow fields with discontinuity in properties, in cases such as shock...

We investigate the class of splitting distributions as composition a singular multivariate distribution and univariate distribution. It will be shown that most common parametric count (multinomial, negative multinomial, hypergeometric, …) can written with separate parameters for both components, thus facilitating their interpretation, inference, study probabilistic characteristics extensions to...

The aim of this study is investigation on effect of stem end splitting and Geranium essential oil on vase life on quality of cut chrysanthemum (Dendranthema grandiflorum L.). This experiment arranged as factorial based on RCD with 2 factors of stem end splitting at 2 levels (with splitting and without splitting) and Geranium essential oil at 6 levels (0, 1, 2, 4, 8 and 10 %), with 12 treatments...

Data splitting divides data into two parts. One part is reserved for model selection. In some applications, the second part is used for model validation but we use this part for estimating the parameters of the chosen model. We focus on the problem of constructing reliable predictive distributions for future observed values. We judge the predictive performance using log scoring. We compare the ...