plot_HMTL

Plot a diagram to illustrate the change of Bayesian information criterion with different penalty paameters for model selection

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Yuan Zhong

HMTL

Heterogeneous Multi-Task Feature Learning

Wei Xu

Xin Gao

plot_HMTL function

<dl><dt>x</dt>
<dd>Object of class "Model Selection" created by <code>Selection_HMTL</code></dd></dl>

Arguments

Plot diagram of the information criterion vs. penalty parameters — plot_HMTL

<dl>

<dt>x</dt>
<dd>Object of class "Model Selection" created by <code>Selection_HMTL</code></dd>

</dl>

plot_HMTL: Plot diagram of the information criterion vs. penalty parameters

Description

Usage

Value

Arguments