datatable.models.LinearModel.__init__()

datatable.models.

LinearModel

(

eta0=0.005

,

eta_decay=0.0001

,

eta_drop_rate=10.0

,

eta_schedule='constant'

,

lambda1=0

,

lambda2=0

,

nepochs=1

,

double_precision=False

,

negative_class=False

,

model_type='auto'

,

seed=0

,

params=None

)

sourcetests

Create a new LinearModel object.

Parameters

eta0

float

The initial learning rate, should be positive.

eta_decay

float

Decay for the "time-based" and "step-based" learning rate schedules, should be non-negative.

eta_drop_rate

float

Drop rate for the "step-based" learning rate schedule, should be positive.

eta_schedule

"constant" | "time-based" | "step-based" | "exponential"

Learning rate schedule. When it is "constant" the learning rate eta is constant and equals to eta0. Otherwise, after each training iteration eta is updated as follows:

• for "time-based" schedule as eta0 / (1 + eta_decay * epoch);

• for "step-based" schedule as eta0 * eta_decay ^ floor((1 + epoch) / eta_drop_rate);

• for "exponential" schedule as eta0 / exp(eta_decay * epoch).

By default, the size of the training iteration is one epoch, it becomes nepochs_validation when validation dataset is specified.

lambda1

float

L1 regularization parameter, should be non-negative.

lambda2

float

L2 regularization parameter, should be non-negative.

nepochs

float

Number of training epochs, should be non-negative. When nepochs is an integer number, the model will train on all the data provided to .fit() method nepochs times. If nepochs has a fractional part {nepochs}, the model will train on all the data [nepochs] times, i.e. the integer part of nepochs. Plus, it will also perform an additional training iteration on the {nepochs} fraction of data.

double_precision

bool

An option to indicate whether double precision, i.e. float64, or single precision, i.e. float32, arithmetic should be used for computations. It is not guaranteed that setting double_precision to True will automatically improve the model accuracy. It will, however, roughly double the memory footprint of the LinearModel object.

negative_class

bool

An option to indicate if a “negative” class should be created in the case of multinomial classification. For the “negative” class the model will train on all the negatives, and if a new label is encountered in the target column, its coefficients will be initialized to the current “negative” class coefficients. If negative_class is set to False, the initial coefficients become zeros.

model_type

"binomial" | "multinomial" | "regression" | "auto"

The model type to be built. When this option is "auto" then the model type will be automatically chosen based on the target column stype.

seed

int

Seed for the quasi-random number generator that is used for data shuffling when fitting the model, should be non-negative. If seed is zero, no shuffling is performed.

params

LinearModelParams

Named tuple of the above parameters. One can pass either this tuple, or any combination of the individual parameters to the constructor, but not both at the same time.

except

ValueError

The exception is raised if both the params and one of the individual model parameters are passed at the same time.