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# Tune a Factorization Machines Model

*Automatic model tuning*, also known as
hyperparameter tuning, finds the best version of a model by running many jobs that
test
a range of hyperparameters on your dataset. You choose the tunable hyperparameters,
a
range of values for each, and an objective metric. You choose the objective metric
from
the metrics that the algorithm computes. Automatic model tuning searches the
hyperparameters chosen to find the combination of values that result in the model
that
optimizes the objective metric.

For more information about model tuning, see Automatic Model Tuning.

## Metrics Computed by the Factorization Machines Algorithm

The factorization machines algorithm has both binary classification and regression
predictor types. The predictor type determines which metric you can use for
automatic model tuning. The algorithm reports a `test:rmse`

regressor
metric, which is computed during training. When tuning the model for regression
tasks, choose this metric as the objective.

Metric Name | Description | Optimization Direction |
---|---|---|

`test:rmse` |
Root Mean Square Error |
Minimize |

The factorization machines algorithm reports three binary classification metrics, which are computed during training. When tuning the model for binary classification tasks, choose one of these as the objective.

Metric Name | Description | Optimization Direction |
---|---|---|

`test:binary_classification_accuracy` |
Accuracy |
Maximize |

`test:binary_classification_cross_entropy` |
Cross Entropy |
Minimize |

`test:binary_f_beta` |
Beta |
Maximize |

## Tunable Factorization Machines Hyperparameters

You can tune the following hyperparameters for the factorization machines
algorithm. The initialization parameters that contain the terms bias, linear, and
factorization depend on their initialization method. There are three initialization
methods: `uniform`

, `normal`

, and `constant`

. These
initialization methods are not themselves tunable. The parameters that are tunable
are dependent on this choice of the initialization method. For example, if the
initialization method is `uniform`

, then only the `scale`

parameters are tunable. Specifically, if `bias_init_method==uniform`

,
then `bias_init_scale`

, `linear_init_scale`

, and
`factors_init_scale`

are tunable. Similarly, if the initialization
method is `normal`

, then only `sigma`

parameters are tunable.
If the initialization method is `constant`

, then only `value`

parameters are tunable. These dependencies are listed in the following table.

Parameter Name | Parameter Type | Recommended Ranges | Dependency |
---|---|---|---|

`bias_init_scale` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==uniform |

`bias_init_sigma` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==normal |

`bias_init_value` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==constant |

`bias_lr` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
None |

`bias_wd` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
None |

`epoch` |
IntegerParameterRange |
MinValue: 1, MaxValue: 1000 |
None |

`factors_init_scale` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==uniform |

`factors_init_sigma` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==normal |

`factors_init_value` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==constant |

`factors_lr` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
None |

`factors_wd` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512] |
None |

`linear_init_scale` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==uniform |

`linear_init_sigma` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==normal |

`linear_init_value` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
bias_init_method==constant |

`linear_lr` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
None |

`linear_wd` |
ContinuousParameterRange |
MinValue: 1e-8, MaxValue: 512 |
None |

`mini_batch_size` |
IntegerParameterRange |
MinValue: 100, MaxValue: 10000 |
None |