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.TH mlpack_bayesian_linear_regression 1 "11 January 2024" "mlpack-4.3.0" "User Commands"
.SH NAME
\fBmlpack_bayesian_linear_regression \fP- bayesianlinearregression
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_bayesian_linear_regression\fP [\fB-c\fP \fIbool\fP] [\fB-i\fP \fIunknown\fP] [\fB-m\fP \fIunknown\fP] [\fB-r\fP \fIunknown\fP] [\fB-s\fP \fIbool\fP] [\fB-t\fP \fIunknown\fP] [\fB-V\fP \fIbool\fP] [\fB-M\fP \fIunknown\fP] [\fB-o\fP \fIunknown\fP] [\fB-u\fP \fIunknown\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


An implementation of the bayesian linear regression.
This model is a probabilistic view and implementation of the linear
regression. The final solution is obtained by computing a posterior
distribution from gaussian likelihood and a zero mean gaussian isotropic 
prior distribution on the solution. 
Optimization is AUTOMATIC and does not require cross validation. The
optimization is performed by maximization of the evidence function. Parameters
are tuned during the maximization of the marginal likelihood. This procedure
includes the Ockham's razor that penalizes over complex solutions. 
.PP
This program is able to train a Bayesian linear regression model or load a
model from file, output regression predictions for a test set, and save the
trained model to a file.
.PP
To train a BayesianLinearRegression model, the '\fB--input_file\fP (\fB-i\fP)' and
\(cq\fB--responses_file\fP (\fB-r\fP)'parameters must be given. The '\fB--center\fP (\fB-c\fP)'and
\(cq\fB--scale\fP (\fB-s\fP)' parameters control the centering and the normalizing options. A
trained model can be saved with the '\fB--output_model_file\fP (\fB-M\fP)'. If no training
is desired at all, a model can be passed via the '\fB--input_model_file\fP (\fB-m\fP)'
parameter.
.PP
The program can also provide predictions for test data using either the
trained model or the given input model. Test points can be specified with the
\(cq\fB--test_file\fP (\fB-t\fP)' parameter. Predicted responses to the test points can be
saved with the '\fB--predictions_file\fP (\fB-o\fP)' output parameter. The corresponding
standard deviation can be save by precising the '\fB--stds_file\fP (\fB-u\fP)' parameter.
.PP
For example, the following command trains a model on the data 'data.csv' and
responses 'responses.csv'with center set to true and scale set to false (so,
Bayesian linear regression is being solved, and then the model is saved to
\(cqblr_model.bin':
.PP
$ \fBmlpack_bayesian_linear_regression\fP \fB--input_file\fP data.csv \fB--responses_file\fP
responses.csv \fB--center\fP \fB--scale\fP \fB--output_model_file\fP blr_model.bin
.PP
The following command uses the 'blr_model.bin' to provide predicted responses
for the data 'test.csv' and save those responses to 'test_predictions.csv': 
.PP
$ \fBmlpack_bayesian_linear_regression\fP \fB--input_model_file\fP blr_model.bin
\fB--test_file\fP test.csv \fB--predictions_file\fP test_predictions.csv
.PP
Because the estimator computes a predictive distribution instead of a simple
point estimate, the '\fB--stds_file\fP (\fB-u\fP)' parameter allows one to save the
prediction uncertainties: 
.PP
$ \fBmlpack_bayesian_linear_regression\fP \fB--input_model_file\fP blr_model.bin
\fB--test_file\fP test.csv \fB--predictions_file\fP test_predictions.csv \fB--stds_file\fP
stds.csv
.RE
.PP

.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--center\fP (\fB-c\fP) [\fIbool\fP]
Center the data and fit the intercept if enabled. 
.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [string]
Print help on a specific option. Default value ''. 
.TP
.B
\fB--input_file\fP (\fB-i\fP) [\fIunknown\fP]
Matrix of covariates (X). 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Trained BayesianLinearRegression model to use. 
.TP
.B
\fB--responses_file\fP (\fB-r\fP) [\fIunknown\fP]
Matrix of responses/observations (y). 
.TP
.B
\fB--scale\fP (\fB-s\fP) [\fIbool\fP]
Scale each feature by their standard deviations if enabled. 
.TP
.B
\fB--test_file\fP (\fB-t\fP) [\fIunknown\fP]
Matrix containing points to regress on (test points). 
.TP
.B
\fB--verbose\fP (\fB-v\fP) [\fIbool\fP]
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP) [\fIbool\fP]
Display the version of mlpack.  
.SH OPTIONAL OUTPUT OPTIONS 

.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
Output BayesianLinearRegression model. 
.TP
.B
\fB--predictions_file\fP (\fB-o\fP) [\fIunknown\fP]
If \fB--test_file\fP is specified, this file is where the predicted responses will be saved. 
.TP
.B
\fB--stds_file\fP (\fB-u\fP) [\fIunknown\fP]
If specified, this is where the standard deviations of the predictive distribution will be saved.
.SH ADDITIONAL INFORMATION

For further information, including relevant papers, citations, and theory,
consult the documentation found at http://www.mlpack.org or included with your
distribution of mlpack.