sparklyr::ml_logistic_regression() fits a model that uses linear predictors to predict multiclass data using the multinomial distribution.

Details

For this engine, there is a single mode: classification

Tuning Parameters

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)

For penalty, the amount of regularization includes both the L1 penalty (i.e., lasso) and the L2 penalty (i.e., ridge or weight decay).

A value of mixture = 1 corresponds to a pure lasso model, while mixture = 0 indicates ridge regression.

Translation from parsnip to the original package

multinom_reg(penalty = double(1), mixture = double(1)) %>% 
  set_engine("spark") %>% 
  translate()

## 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(), 
##     weight_col = missing_arg(), reg_param = double(1), elastic_net_param = double(1), 
##     family = "multinomial")

Preprocessing requirements

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.model_spec(), 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. By default, ml_multinom_regression() uses the argument standardization = TRUE to center and scale the data.

Other details

For models created using the "spark" engine, there are several things to consider.

  • Only the formula interface to via fit() is available; using fit_xy() will generate an error.

  • 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 save()), the model$fit element of the parsnip object should be serialized via ml_save(object$fit) and separately saved to disk. In a new session, the object can be reloaded and reattached to the parsnip object.

References

  • Luraschi, J, K Kuo, and E Ruiz. 2019. Mastering Spark with R. O’Reilly Media

  • Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.

  • Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.