RMSE

Computes the Root Mean Squared Error (RMSE), a widely used metric for evaluating the accuracy of predictions in regression tasks. The formula is given by
$$RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}$$

A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.

Mateus Maia

randomMachines

An Ensemble Modeling using Random Machines

Anderson Ara

Gabriel Ribeiro

RMSE function

<dl><dt>predicted</dt>
<dd>A vector of predicted values $\hat{\mathbf{y}}$.</dd>
<dt>observed</dt>
<dd>A vector of observed values $\mathbf{y}$.</dd></dl>

Arguments

Root Mean Squared Error (RMSE) Function — RMSE

<dl>

<dt>predicted</dt>
<dd>A vector of predicted values $\hat{\mathbf{y}}$.</dd>


<dt>observed</dt>
<dd>A vector of observed values $\mathbf{y}$.</dd>

</dl>

RMSE: Root Mean Squared Error (RMSE) Function

Description

Usage

Value

Arguments