# ml_gbt_classifier

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Percentile

##### Spark ML -- Gradient Boosted Trees

Perform binary classification and regression using gradient boosted trees. Multiclass classification is not supported yet.

##### Usage
ml_gbt_classifier(x, formula = NULL, max_iter = 20, max_depth = 5,
step_size = 0.1, subsampling_rate = 1,
feature_subset_strategy = "auto", min_instances_per_node = 1L,
max_bins = 32, min_info_gain = 0, loss_type = "logistic",
seed = NULL, thresholds = NULL, checkpoint_interval = 10,
cache_node_ids = FALSE, max_memory_in_mb = 256,
features_col = "features", label_col = "label",
prediction_col = "prediction", probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("gbt_classifier_"), ...)ml_gradient_boosted_trees(x, formula = NULL, type = c("auto",
"regression", "classification"), features_col = "features",
label_col = "label", prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction", checkpoint_interval = 10,
loss_type = c("auto", "logistic", "squared", "absolute"),
max_bins = 32, max_depth = 5, max_iter = 20L, min_info_gain = 0,
min_instances_per_node = 1, step_size = 0.1, subsampling_rate = 1,
feature_subset_strategy = "auto", seed = NULL, thresholds = NULL,
cache_node_ids = FALSE, max_memory_in_mb = 256,
uid = random_string("gradient_boosted_trees_"), response = NULL,
features = NULL, ...)ml_gbt_regressor(x, formula = NULL, max_iter = 20, max_depth = 5,
step_size = 0.1, subsampling_rate = 1,
feature_subset_strategy = "auto", min_instances_per_node = 1,
max_bins = 32, min_info_gain = 0, loss_type = "squared",
seed = NULL, checkpoint_interval = 10, cache_node_ids = FALSE,
max_memory_in_mb = 256, features_col = "features",
label_col = "label", prediction_col = "prediction",
uid = random_string("gbt_regressor_"), ...)
##### Arguments
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.

max_iter

Maxmimum number of iterations.

max_depth

Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.

step_size

Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)

subsampling_rate

Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

feature_subset_strategy

The number of features to consider for splits at each tree node. See details for options.

min_instances_per_node

Minimum number of instances each child must have after split.

max_bins

The maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.

min_info_gain

Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.

loss_type

Loss function which GBT tries to minimize. Supported: "squared" (L2) and "absolute" (L1) (default = squared) for regression and "logistic" (default) for classification. For ml_gradient_boosted_trees, setting "auto" will default to the appropriate loss type based on model type.

seed

Seed for random numbers.

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.

checkpoint_interval

Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.

cache_node_ids

If FALSE, the algorithm will pass trees to executors to match instances with nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Defaults to FALSE.

max_memory_in_mb

Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. Defaults to 256.

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.

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.

type

The type of model to fit. "regression" treats the response as a continuous variable, while "classification" treats the response as a categorical variable. When "auto" is used, the model type is inferred based on the response variable type -- if it is a numeric type, then regression is used; classification otherwise.

response

(Deprecated) The name of the response column (as a length-one character vector.)

features

(Deprecated) The name of features (terms) to use for the model fit.

##### Details

When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.

The supported options for feature_subset_strategy are

• "auto": Choose automatically for task: If num_trees == 1, set to "all". If num_trees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.

• "all": use all features

• "onethird": use 1/3 of the features

• "sqrt": use use sqrt(number of features)

• "log2": use log2(number of features)

• "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

ml_gradient_boosted_trees is a wrapper around ml_gbt_regressor.tbl_spark and ml_gbt_classifier.tbl_spark and calls the appropriate method based on model type.

##### Value

The object returned depends on the class of x.

• spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Predictor object and can be used to compose Pipeline objects.

• ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the predictor appended to the pipeline.

• tbl_spark: When x is a tbl_spark, a predictor is constructed then immediately fit with the input tbl_spark, returning a prediction model.

• tbl_spark, with formula: specified When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the predictor. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model.

See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.

Other ml algorithms: ml_aft_survival_regression, ml_decision_tree_classifier, ml_generalized_linear_regression, ml_isotonic_regression, ml_linear_regression, ml_linear_svc, ml_logistic_regression, ml_multilayer_perceptron_classifier, ml_naive_bayes, ml_one_vs_rest, ml_random_forest_classifier

##### Aliases
• ml_gbt_classifier
• ml_gbt_regressor
##### Examples
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

iris_training <- partitions$training iris_test <- partitions$test

gbt_model <- iris_training %>%