smote

Synthetic Minority Oversampling Technique (SMOTE) algorithm for imbalanced
classification data.

Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.

Myles Lewis

nestedcv

Nested Cross-Validation with 'glmnet' and 'caret'

Athina Spiliopoulou

Cankut Cubuk

Katriona Goldmann

smote function

<dl><dt>y</dt>
<dd>Vector of response outcome as a factor</dd>
<dt>x</dt>
<dd>Matrix of predictors</dd>
<dt>k</dt>
<dd>Range of KNN to consider for generation of new data</dd>
<dt>over</dt>
<dd>Amount of oversampling of the minority class. If set to <code>NULL</code>
then all classes will be oversampled up to the number of samples in the
majority class.</dd>
<dt>yminor</dt>
<dd>Optional character value specifying the level in <code>y</code> which is
to be oversampled. If <code>NULL</code>, this is set automatically to the class with
the smallest sample size.</dd></dl>

Arguments

SMOTE — smote

<dl>

<dt>y</dt>
<dd>Vector of response outcome as a factor</dd>


<dt>x</dt>
<dd>Matrix of predictors</dd>


<dt>k</dt>
<dd>Range of KNN to consider for generation of new data</dd>


<dt>over</dt>
<dd>Amount of oversampling of the minority class. If set to <code>NULL</code>
then all classes will be oversampled up to the number of samples in the
majority class.</dd>


<dt>yminor</dt>
<dd>Optional character value specifying the level in <code>y</code> which is
to be oversampled. If <code>NULL</code>, this is set automatically to the class with
the smallest sample size.</dd>

</dl>

smote: SMOTE

Description

Usage

Value

Arguments

References