ML SIGW0: Difference between revisions

Revision as of 15:46, 3 July 2023

ML_SIGW0 = [real]
Default: none

Default: ML_SIGW0	= 1E-7	for ML_MODE = REFIT
	= 1.0	else

Description: This flag sets the precision parameter $s_{\mathrm {w} }$ for the fitting in the machine learning force field method.

If the regularization needs to be controlled manually, like e.g. in the fitting via singular value decomposition (ML_IALGO_LINREG=4), the best is to control the regularization via this parameter and keep the noise paramter $s_{\mathrm {v} }$ (see ML_SIGV0) constant at 1.

For the theory of this regularization parameter see this section.

@@ Line 3: / Line 3: @@
 {{DEF|ML_SIGW0|1E-7|for {{TAG|ML_MODE}} {{=}} REFIT|1.0|else}}
-Description: This flag sets the initial reversed and squared precision parameter <math>\frac{1}{\sigma_{\mathrm{w}}^{2}}</math> in the machine learning force field method.
+Description: This flag sets the precision parameter <math>s_{\mathrm{w}}</math> for the fitting in the machine learning force field method.
 ----
-For details about the optimization of this regularization parameter see [[Machine learning force field: Theory#Bayesian error estimation|this section]].
+If the regularization needs to be controlled manually, like e.g. in the fitting via singular value decomposition ({{TAG|ML_IALGO_LINREG}}=4), the best is to control the regularization via this parameter and keep the noise paramter <math>s_{\mathrm{v}}</math> (see {{TAG|ML_SIGV0}}) constant at 1.
+For the theory of this regularization parameter see [[Machine learning force field: Theory#Regression|this section]].
 == Related tags and articles ==
-{{TAG|ML_LMLFF}}, {{TAG|ML_MODE}}, {{TAG|ML_IREG}}, {{TAG|ML_SIGV0}}
+{{TAG|ML_LMLFF}}, {{TAG|ML_MODE}}, {{TAG|ML_IREG}}, {{TAG|ML_SIGV0}}, {{TAG|ML_IALGO_LINREG}}
 {{sc|ML_SIGW0|Examples|Examples that use this tag}}