Multinomial regression via sparkSource:
sparklyr::ml_logistic_regression() fits a model that uses linear
predictors to predict multiclass data using the multinomial distribution.
For this engine, there is a single mode: classification
This model has 2 tuning parameters:
penalty: Amount of Regularization (type: double, default: 0.0)
mixture: Proportion of Lasso Penalty (type: double, default: 0.0)
penalty, the amount of regularization includes both the L1 penalty
(i.e., lasso) and the L2 penalty (i.e., ridge or weight decay). As for
mixture = 1specifies a pure lasso model,
mixture = 0specifies a ridge regression model, and
0 < mixture < 1specifies an elastic net model, interpolating lasso and ridge.
## Multinomial Regression Model Specification (classification) ## ## Main Arguments: ## penalty = double(1) ## mixture = double(1) ## ## Computational engine: spark ## ## Model fit template: ## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(), ## weights = missing_arg(), reg_param = double(1), elastic_net_param = double(1), ## family = "multinomial")
Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via
fit(), parsnip will
convert factor columns to indicators.
Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.
ml_multinom_regression() uses the argument
standardization = TRUE to center and scale the data.
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
Note that, for spark engines, the
case_weight argument value should be
a character string to specify the column with the numeric case weights.
For models created using the
"spark" engine, there are several things
The predictions will always be in a Spark table format. The names will be the same as documented but without the dots.
There is no equivalent to factor columns in Spark tables so class predictions are returned as character columns.
To retain the model object for a new R session (via
model$fitelement of the parsnip object should be serialized via
ml_save(object$fit)and separately saved to disk. In a new session, the object can be reloaded and reattached to the parsnip object.