Split2TrainTest

an n-by-1 vector of responses or labels.

an n-by-p design matrix of predictors.

a ratio of the size of the resulting training set to the size of data.

train.ratio

Convert data into training and test sets so that the training set contains approximately the specified ratio of all labels.

Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber (2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al. (1998) <DOI:10.1109/5.726791> for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data.

Jiwoong Kim

Buddle

A Deep Learning for Statistical Classification and Regression
Analysis with Random Effects

Split2TrainTest function

<dl><dt>Y</dt>
<dd>an n-by-1 vector of responses or labels.</dd>
<dt>X</dt>
<dd>an n-by-p design matrix of predictors.</dd>
<dt>train.ratio</dt>
<dd>a ratio of the size of the resulting training set to the size of data.</dd></dl>

Arguments

Splitting Data into Training and Test Sets. — Split2TrainTest

<dl>

<dt>Y</dt>
<dd>an n-by-1 vector of responses or labels.</dd>


<dt>X</dt>
<dd>an n-by-p design matrix of predictors.</dd>


<dt>train.ratio</dt>
<dd>a ratio of the size of the resulting training set to the size of data.</dd>

</dl>

Split2TrainTest: Splitting Data into Training and Test Sets.

Description

Usage

Value

Arguments

See Also

Examples