---
name: "mlpack_linear_svm"
section: "1"
platform: "linux"
package: "mlpack-bin"
version: "4.6.0-1"
release: "Debian 13 (Trixie)"
platforms: ["linux"]
url: "https://new.carta.tech/mlpack_linear_svm"
---

# mlpack\_linear\_svm(1)

linear svm is an l2-regularized support vector machine.

## NAME

**mlpack\_linear\_svm** - linear svm is an l2-regularized support vector machine.

## SYNOPSIS

```
 mlpack_linear_svm [-d double] [-E int] [-m unknown] [-l unknown] [-r double] [-n int] [-N bool] [-c int] [-O string] [-s int] [-S bool] [-a double] [-T unknown] [-L unknown] [-e double] [-t unknown] [-V bool] [-M unknown] [-P unknown] [-p unknown] [-h -v]
```

## DESCRIPTION

An implementation of linear SVMs that uses either L-BFGS or parallel SGD (stochastic gradient descent) to train the model.

This program allows loading a linear SVM model (via the '**--input\_model\_file** (**-m**)' parameter) or training a linear SVM model given training data (specified with the '**--training\_file** (**-t**)' parameter), or both those things at once. In addition, this program allows classification on a test dataset (specified with the '**--test\_file** (**-T**)' parameter) and the classification results may be saved with the '**--predictions\_file** (**-P**)' output parameter. The trained linear SVM model may be saved using the '**--output\_model\_file** (**-M**)' output parameter.

The training data, if specified, may have class labels as its last dimension.  Alternately, the '**--labels\_file** (**-l**)' parameter may be used to specify a separate vector of labels.

When a model is being trained, there are many options. L2 regularization (to prevent overfitting) can be specified with the '**--lambda** (**-r**)' option, and the number of classes can be manually specified with the '**--num\_classes** (**-c**)'and if an intercept term is not desired in the model, the '**--no\_intercept** (**-N**)' parameter can be specified.Margin of difference between correct class and other classes can be specified with the '**--delta** (**-d**)' option.The optimizer used to train the model can be specified with the '**--optimizer** (**-O**)' parameter. Available options are 'psgd' (parallel stochastic gradient descent) and 'lbfgs' (the L-BFGS optimizer). There are also various parameters for the optimizer; the '**--max\_iterations** (**-n**)' parameter specifies the maximum number of allowed iterations, and the '**--tolerance** (**-e**)' parameter specifies the tolerance for convergence. For the parallel SGD optimizer, the ’**--step\_size** (**-a**)' parameter controls the step size taken at each iteration by the optimizer and the maximum number of epochs (specified with '**--epochs** (**-E**)'). If the objective function for your data is oscillating between Inf and 0, the step size is probably too large. There are more parameters for the optimizers, but the C++ interface must be used to access these.

Optionally, the model can be used to predict the labels for another matrix of data points, if '**--test\_file** (**-T**)' is specified. The '**--test\_file** (**-T**)' parameter can be specified without the '**--training\_file** (**-t**)' parameter, so long as an existing linear SVM model is given with the '**--input\_model\_file** (**-m**)' parameter. The output predictions from the linear SVM model may be saved with the '**--predictions\_file** (**-P**)' parameter.

As an example, to train a LinaerSVM on the data ''data.csv'' with labels ’'labels.csv'' with L2 regularization of 0.1, saving the model to ’'lsvm\_model.bin'', the following command may be used:

$ **mlpack\_linear\_svm** **--training\_file** data.csv **--labels\_file** labels.csv **--lambda** 0.1 **--delta** 1 **--num\_classes** 0 **--output\_model\_file** lsvm\_model.bin

Then, to use that model to predict classes for the dataset ''test.csv'', storing the output predictions in ''predictions.csv'', the following command may be used:

$ **mlpack\_linear\_svm** **--input\_model\_file** lsvm\_model.bin **--test\_file** test.csv **--predictions\_file** predictions.csv

## OPTIONAL INPUT OPTIONS

- **--delta (-d) \[*double*\]**

  Margin of difference between correct class and other classes. Default value 1.

- **--epochs (-E) \[*int*\]**

  Maximum number of full epochs over dataset for psgd Default value 50.

- **--help (-h) \[*bool*\]**

  Default help info.

- **--info \[*string*\]**

  Print help on a specific option. Default value ''.

- **--input\_model\_file (-m) \[*unknown*\]**

  Existing model (parameters).  **--labels\_file** (**-l**) \[*unknown*\] A matrix containing labels (0 or 1) for the points in the training set (y).

- **--lambda (-r) \[*double*\]**

  L2-regularization parameter for training.  Default value 0.0001.

- **--max\_iterations (-n) \[*int*\]**

  Maximum iterations for optimizer (0 indicates no limit). Default value 10000.

- **--no\_intercept (-N) \[*bool*\]**

  Do not add the intercept term to the model.

- **--num\_classes (-c) \[*int*\]**

  Number of classes for classification; if unspecified (or 0), the number of classes found in the labels will be used. Default value 0.

- **--optimizer (-O) \[*string*\]**

  Optimizer to use for training ('lbfgs' or 'psgd'). Default value 'lbfgs'.

- **--seed (-s) \[*int*\]**

  Random seed. If 0, 'std::time(NULL)' is used.  Default value 0.

- **--shuffle (-S) \[*bool*\]**

  Don't shuffle the order in which data points are visited for parallel SGD.

- **--step\_size (-a) \[*double*\]**

  Step size for parallel SGD optimizer. Default value 0.01.

- **--test\_file (-T) \[*unknown*\]**

  Matrix containing test dataset.

- **--test\_labels\_file (-L) \[*unknown*\]**

  Matrix containing test labels.

- **--tolerance (-e) \[*double*\]**

  Convergence tolerance for optimizer. Default value 1e-10.

- **--training\_file (-t) \[*unknown*\]**

  A matrix containing the training set (the matrix of predictors, X).

- **--verbose (-v) \[*bool*\]**

  Display informational messages and the full list of parameters and timers at the end of execution.

- **--version (-V) \[*bool*\]**

  Display the version of mlpack.

## OPTIONAL OUTPUT OPTIONS

- **--output\_model\_file (-M) \[*unknown*\]**

  Output for trained linear svm model.

- **--predictions\_file (-P) \[*unknown*\]**

  If test data is specified, this matrix is where the predictions for the test set will be saved.

- **--probabilities\_file (-p) \[*unknown*\]**

  If test data is specified, this matrix is where the class probabilities for the test set will be saved.

## 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.