Spark ML - Logistic Regression
ml_logistic_regression
Description
Perform classification using logistic regression.
Usage
ml_logistic_regression(
x,
formula = NULL,
fit_intercept = TRUE,
elastic_net_param = 0,
reg_param = 0,
max_iter = 100,
threshold = 0.5,
thresholds = NULL,
tol = 1e-06,
weight_col = NULL,
aggregation_depth = 2,
lower_bounds_on_coefficients = NULL,
lower_bounds_on_intercepts = NULL,
upper_bounds_on_coefficients = NULL,
upper_bounds_on_intercepts = NULL,
features_col = "features",
label_col = "label",
family = "auto",
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("logistic_regression_"),
...
)Arguments
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_spark. |
| formula | Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details. |
| fit_intercept | Boolean; should the model be fit with an intercept term? |
| elastic_net_param | ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. |
| reg_param | Regularization parameter (aka lambda) |
| max_iter | The maximum number of iterations to use. |
| threshold | in binary classification prediction, in range [0, 1]. |
| thresholds | Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class’s threshold. |
| tol | Param for the convergence tolerance for iterative algorithms. |
| weight_col | The name of the column to use as weights for the model fit. |
| aggregation_depth | (Spark 2.1.0+) Suggested depth for treeAggregate (>= 2). |
| lower_bounds_on_coefficients | (Spark 2.2.0+) Lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. |
| lower_bounds_on_intercepts | (Spark 2.2.0+) Lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression. |
| upper_bounds_on_coefficients | (Spark 2.2.0+) Upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. |
| upper_bounds_on_intercepts | (Spark 2.2.0+) Upper bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression. |
| features_col | Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula. |
| label_col | Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula. |
| family | (Spark 2.1.0+) Param for the name of family which is a description of the label distribution to be used in the model. Supported options: “auto”, “binomial”, and “multinomial.” |
| prediction_col | Prediction column name. |
| probability_col | Column name for predicted class conditional probabilities. |
| raw_prediction_col | Raw prediction (a.k.a. confidence) column name. |
| uid | A character string used to uniquely identify the ML estimator. |
| … | Optional arguments; see Details. |
Value
The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.
See Also
Other ml algorithms: ml_aft_survival_regression(), ml_decision_tree_classifier(), ml_gbt_classifier(), ml_generalized_linear_regression(), ml_isotonic_regression(), ml_linear_regression(), ml_linear_svc(), ml_multilayer_perceptron_classifier(), ml_naive_bayes(), ml_one_vs_rest(), ml_random_forest_classifier()
Examples
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)
partitions <- mtcars_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
mtcars_training <- partitions$training
mtcars_test <- partitions$test
lr_model <- mtcars_training %>%
ml_logistic_regression(am ~ gear + carb)
pred <- ml_predict(lr_model, mtcars_test)
ml_binary_classification_evaluator(pred)