.\" Text automatically generated by txt2man
.TH mlpack_linear_regression 1 "12 December 2020" "mlpack-3.4.2" "User Commands"
.SH NAME
\fBmlpack_linear_regression \fP- simple linear regression and prediction
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_linear_regression\fP [\fB-m\fP \fIunknown\fP] [\fB-l\fP \fIdouble\fP] [\fB-T\fP \fIstring\fP] [\fB-t\fP \fIstring\fP] [\fB-r\fP \fIstring\fP] [\fB-V\fP \fIbool\fP] [\fB-M\fP \fIunknown\fP] [\fB-o\fP \fIstring\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


An implementation of simple linear regression and simple ridge regression
using ordinary least squares. This solves the problem
.PP
.nf
.fam C
  y = X * b + e

.fam T
.fi
where X (specified by '\fB--training_file\fP (\fB-t\fP)') and y (specified either as the
last column of the input matrix '\fB--training_file\fP (\fB-t\fP)' or via the
\(cq\fB--training_responses_file\fP (\fB-r\fP)' parameter) are known and b is the desired
variable. If the covariance matrix (X'X) is not invertible, or if the
solution is overdetermined, then specify a Tikhonov regularization constant
(with '\fB--lambda\fP (\fB-l\fP)') greater than 0, which will regularize the covariance
matrix to make it invertible. The calculated b may be saved with the
\(cq\fB--output_predictions_file\fP (\fB-o\fP)' output parameter.
.PP
Optionally, the calculated value of b is used to predict the responses for
another matrix X' (specified by the '\fB--test_file\fP (\fB-T\fP)' parameter):
.PP
.nf
.fam C
   y' = X' * b

.fam T
.fi
and the predicted responses y' may be saved with the
\(cq\fB--output_predictions_file\fP (\fB-o\fP)' output parameter. This type of regression is
related to least-angle regression, which mlpack implements as the 'lars'
program.
.PP
For example, to run a linear regression on the dataset 'X.csv' with responses
\(cqy.csv', saving the trained model to 'lr_model.bin', the following command
could be used:
.PP
$ \fBmlpack_linear_regression\fP \fB--training_file\fP X.csv \fB--training_responses_file\fP
y.csv \fB--output_model_file\fP lr_model.bin
.PP
Then, to use 'lr_model.bin' to predict responses for a test set 'X_test.csv',
saving the predictions to 'X_test_responses.csv', the following command could
be used:
.PP
$ \fBmlpack_linear_regression\fP \fB--input_model_file\fP lr_model.bin \fB--test_file\fP
X_test.csv \fB--output_predictions_file\fP X_test_responses.csv
.RE
.PP

.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Print help on a specific option. Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Existing LinearRegression model to use. 
.TP
.B
\fB--lambda\fP (\fB-l\fP) [\fIdouble\fP]
Tikhonov regularization for ridge regression.  If 0, the method reduces to linear regression.  Default value 0. 
.TP
.B
\fB--test_file\fP (\fB-T\fP) [\fIstring\fP]
Matrix containing X' (test regressors). 
.TP
.B
\fB--training_file\fP (\fB-t\fP) [\fIstring\fP]
Matrix containing training set X (regressors). 
.TP
.B
\fB--training_responses_file\fP (\fB-r\fP) [\fIstring\fP]
Optional vector containing y (responses). If not given, the responses are assumed to be the last row of the input file. 
.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 LinearRegression model. 
.TP
.B
\fB--output_predictions_file\fP (\fB-o\fP) [\fIstring\fP]
If \fB--test_file\fP is specified, this matrix is where the predicted responses 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.