Commit 632a3a00 authored by Francesco Fracchia's avatar Francesco Fracchia

Update README.rst

parent c8088d17
......@@ -30,8 +30,8 @@ Weigthed Linear Ridge Regression
Purpose of the Module
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This module solves the weighted linear ridge regression problem calculating the linear parameters of a model selected by the user that minimize the deviations of the predictions from the references of the data set. Therefore, it is a supervised learning tool that optimizes the linear parameters of a analytical expression in order to fit a data set. Each element of the data set can be weighted according to the relative importance or reliability attributed by the user. The regularization provides a protection from the overfitting, this inconvinient can occur if the flexibility of the model is too high in relation to the available data. Moreover, the module calculates the leave-one-out cross-validation error for the employed data set.
The WLRR module is a component of the LRR-DE software tool, developed to parametrize force fields of metal ions. In the LRR-DE software tool, the WLRR module is combined with the metaheuristic optimization algorithm differential evolution in order to tune the hyperparameters of the model (the regularization parameter and the non-linear parameters of the model).
This module solves the weighted linear ridge regression problem calculating the linear parameters of a model selected by the user that minimize the deviations of the predictions from the references of the data set. Therefore, it is a supervised learning tool that optimizes the linear parameters of an analytical expression in order to fit a data set. Each element of the data set can be weighted according to the relative importance or reliability attributed by the user. The regularization provides a protection from the over-fitting, this inconvenient can occur if the flexibility of the model is too high in relation to the available data. Moreover, the module calculates the leave-one-out cross-validation error for the employed data set.
The WLRR module is a component of the LRR-DE software tool, developed to parametrize force fields of metal ions. In the LRR-DE software tool, the WLRR module is combined with the metaheuristic optimization algorithm differential evolution in order to tune the hyper-parameters of the model (the regularization parameter and the non-linear parameters of the model).
The LRR-DE module has been developed to parametrize force fields of metal ions, however the method can be applied to optimize the parameters of a general functional form with respect to reference data.
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