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## Re: Model object as output for linear regression in Octave 3.8

**From**: |
Juan Pablo Carbajal |

**Subject**: |
Re: Model object as output for linear regression in Octave 3.8 |

**Date**: |
Mon, 22 Sep 2014 14:50:40 +0200 |

On Mon, Sep 22, 2014 at 1:40 PM, Narayanan, Krishnaprasad
<address@hidden> wrote:
>* Hallo all,*
>
>
>
>* I am using Octave 3.8.0 to build a linear model that has several input*
>* features and one output feature. I will be using this model in order to*
>* predict the output for the given set of input features. When I searched on*
>* the web for statistical packages, I found the following functions: polyfit*
>* and regress. But none of these functions returns me a model object which I*
>* can use it for prediction.*
>
>
>
>* Can I kindly know from the forum is there a function in octave that returns*
>* me a model object for linear regression?*
>
>
>
>* Regards,*
>
>* Krishnaprasad*
>
>
>* _______________________________________________*
>* Help-octave mailing list*
>* address@hidden*
>* https://lists.gnu.org/mailman/listinfo/help-octave*
>
Krishnaprasad
If you want to use linear regression you do not need polyfit. The
function regress indeed returns a model you can use, here a minimalist
example
X = randn(10,5);
y = X*linspace(-1,1,5).' + 0.01*randn(10,1);
B = regress (y, X);
plot(y,'.;data;',X*B,'o;train;')
X_new = randn(100,5);
y_predict = X_new*B
Just make sure you read the help of regress to understand its outputs
and also check your theory on linear regression for more robust
results.
Hope this helps.