r descr_models("logistic_reg", "spark")
defaults <- tibble::tibble(parsnip = c("penalty", "mixture"), default = c("0.0", "0.0")) param <- logistic_reg() %>% set_engine("spark") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
For 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
:
mixture = 1
specifies a pure lasso model,mixture = 0
specifies a ridge regression model, and0 < mixture < 1
specifies an elastic net model, interpolating lasso and ridge.logistic_reg(penalty = double(1), mixture = double(1)) %>% set_engine("spark") %>% translate()
By default, ml_logistic_regression()
uses the argument standardization = TRUE
to center and scale the data.
Note that, for spark engines, the case_weight
argument value should be a character string to specify the column with the numeric case weights.
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.
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