# Linear Learner Hyperparameters

The following table contains the hyperparameters for the learner learner algorithm. These are parameters that are set by users to facilitate the estimation of model parameters from data. The required hyperparameters that must be set are listed first, in alphabetical order. The optional hyperparameters that can be set are listed next, also in alphabetical order.

Parameter Name | Description |
---|---|

`feature_dim` |
The number of features in the input data.
Valid values: Positive integer |

`num_classes` |
The number of classes for the response variable. The algorithm
assumes that classes are labeled
Valid values: Integers from 3 to 1,000,000 |

`predictor_type` |
Specifies the type of target variable as a binary classification, multiclass classification, or regression.
Valid values: |

`accuracy_top_k` |
When computing the top-k accuracy metric for multiclass
classification, the value of
Valid values: Positive integers Default value: 3 |

`balance_multiclass_weights` |
Specifies whether to use class weights, which give each class
equal importance in the loss function. Used only when the
Valid values: Default value: |

`beta_1` |
The exponential decay rate for first-moment estimates. Applies
only when the
Valid values: Default value: |

`beta_2` |
The exponential decay rate for second-moment estimates. Applies
only when the
Valid values: Default value: |

`bias_lr_mult` |
Allows a different learning rate for the bias term. The actual
learning rate for the bias is
Valid values: Default value: |

`bias_wd_mult` |
Allows different regularization for the bias term. The actual L2
regularization weight for the bias is
Valid values: Default value: |

`binary_classifier_model_selection_criteria` |
When -
`accuracy` —The model with the highest accuracy. -
`f_beta` —The model with the highest F1 score. The default is F1. -
`precision_at_target_recall` —The model with the highest precision at a given recall target. -
`recall_at_target_precision` —The model with the highest recall at a given precision target. -
`loss_function` —The model with the lowest value of the loss function used in training.
Valid values: Default value: |

`early_stopping_patience` |
If no improvement is made in the relevant metric, the number of
epochs to wait before ending training. If you have provided a value for
`binary_classifier_model_selection_criteria` . the metric
is that value. Otherwise, the metric is the same as the value specified
for the `loss` hyperparameter.
The metric is evaluated
on the validation data. If you haven't provided validation data, the
metric is always the same as the value specified for the
Valid values: Positive integer Default value: 3 |

`early_stopping_tolerance` |
The relative tolerance to measure an improvement in loss. If the ratio of the improvement in loss divided by the previous best loss is smaller than this value, early stopping considers the improvement to be zero.
Valid values: Positive floating-point integer Default value: 0.001 |

`epochs` |
The maximum number of passes over the training data.
Valid values: Positive integer Default value: 15 |

`f_beta` |
The value of beta to use when calculating F score metrics for
binary or multiclass classification. Also used if the value
specified for
Valid values: Positive floating-point integers Default value: 1.0 |

`huber_delta` |
The parameter for Huber loss. During training and metric evaluation, compute L2 loss for errors smaller than delta and L1 loss for errors larger than delta.
Valid values: Positive floating-point integer Default value: 1.0 |

`init_bias` |
Initial weight for the bias term.
Valid values: Floating-point integer Default value: 0 |

`init_method` |
Sets the initial distribution function used for model weights. Functions include: -
`uniform` —Uniformly distributed between (-scale, +scale) -
`normal` —Normal distribution, with mean 0 and sigma
Valid values: Default value: |

`init_scale` |
Scales an initial uniform distribution for model weights. Applies
only when the
Valid values: Positive floating-point integer Default value: 0.07 |

`init_sigma` |
The initial standard deviation for the normal distribution.
Applies only when the
Valid values: Positive floating-point integer Default value: 0.01 |

`l1` |
The L1 regularization parameter. If you don't want to use L1 regularization, set the value to 0.
Valid values: Default value: |

`learning_rate` |
The step size used by the optimizer for parameter updates.
Valid values: Default value: |

`loss` |
Specifies the loss function. The available loss functions and their default values depend on
the value of -
If the `predictor_type` is set to`regressor` , the available options are`auto` ,`squared_loss` ,`absolute_loss` ,`eps_insensitive_squared_loss` ,`eps_insensitive_absolute_loss` ,`quantile_loss` , and`huber_loss` . The default value for`auto` is`squared_loss` . -
If the `predictor_type` is set to`binary_classifier` , the available options are`auto` ,`logistic` , and`hinge_loss` . The default value for`auto` is`logistic` . -
If the `predictor_type` is set to`multiclass_classifier` , the available options are`auto` and`softmax_loss` . The default value for`auto` is`softmax_loss` .
Valid values:
Default value: |

`loss_insensitivity` |
The parameter for the epsilon-insensitive loss type. During training and metric evaluation, any error smaller than this value is considered to be zero.
Valid values: Positive floating-point integer Default value: 0.01 |

`lr_scheduler_factor` |
For every
Valid values: Default value: |

`lr_scheduler_minimum_lr` |
The learning rate never decreases to a value lower than the value
set for
Valid values: Default values: |

`lr_scheduler_step` |
The number of steps between decreases of the learning rate.
Applies only when the
Valid values: Default value: |

`margin` |
The margin for the
Valid values: Positive floating-point integer Default value: 1.0 |

`mini_batch_size` |
The number of observations per mini-batch for the data iterator.
Valid values: Positive integer Default value: 1000 |

`momentum` |
The momentum of the
Valid values: Default value: |

`normalize_data` |
Normalizes the features before training to achieve a
Valid values: Default value: |

`normalize_label` |
Normalizes the label. The
Valid values: Default value: |

`num_calibration_samples` |
The number of observations from the validation dataset to use for model calibration (when finding the best threshold).
Valid values: Default value: |

`num_models` |
The number of models to train in parallel. For the default,
Valid values: Default values: |

`num_point_for_scaler` |
The number of data points to use for calculating normalization or unbiasing of terms.
Valid values: Positive integer Default value: 10,000 |

`optimizer` |
The optimization algorithm to use.
Valid values: -
`auto` —The default value. -
`sgd` —Stochastic gradient descent. -
`rmsprop` —A gradient-based optimization technique that uses a moving average of squared gradients to normalize the gradient.
Default value: |

`positive_example_weight_mult` |
The weight assigned to positive examples when training a binary
classifier. The weight of negative examples is fixed at 1. If you
want the algorithm to choose a weight so that errors in classifying
negative
Valid values: Default value: 1.0 |

`quantile` |
The quantile for quantile loss. For quantile q, the model attempts
to produce predictions so that the value of
Valid values: Floating-point integer between 0 and 1 Default value: 0.5 |

`target_precision` |
The target precision. If
Valid values: Floating-point integer between 0 and 1.0 Default value: 0.8 |

`target_recall` |
The target recall. If
Valid values: Floating-point integer between 0 and 1.0 Default value: 0.8 |

`unbias_data` |
Unbiases the features before training so that the mean is 0.
By
default. data is unbiased if the
Valid values: Default value: |

`unbias_label` |
Unbiases labels before training so that the mean is 0. Applies to
regression only if the
Valid values: Default value: |

`use_bias` |
Specifies whether the model should include a bias term, which is the intercept term in the linear equation.
Valid values: Default value: |

`use_lr_scheduler` |
Whether to use a scheduler for the learning rate. If you want to
use a scheduler, specify
Valid values: Default value: |

`wd` |
The weight decay parameter, also known as the L2 regularization parameter. If you don't want to use L2 regularization, set the value to 0.
Valid values: Default value: |