ml_lda(x, features = dplyr::tbl_vars(x), k = length(features), alpha = (50/k) + 1, beta = 0.1 + 1, ml.options = ml_options(), ...)tbl_spark).k in fitting (as currently EM optimizer only supports symmetric distributions, so all values in the vector should be the same). For Expectation-Maximization optimizer values should be > 1.0.
By default alpha = (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows from Asuncion et al. (2009), who recommend a +1 adjustment for EM.beta = 0.1 + 1, where 0.1 gives a small amount of smoothing and +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM.ml_options for more details.Asuncion et al. (2009)
ml_als_factorization,
ml_decision_tree,
ml_generalized_linear_regression,
ml_gradient_boosted_trees,
ml_kmeans,
ml_linear_regression,
ml_logistic_regression,
ml_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest, ml_pca,
ml_random_forest,
ml_survival_regression