`R/ml_classification_gbt_classifier.R`

, `R/ml_model_gradient_boosted_trees.R`

, `R/ml_regression_gbt_regressor.R`

`ml_gradient_boosted_trees.Rd`

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

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_"), ... )

x | A |
---|---|

formula | Used when |

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: |

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 |

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 |

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 |

label_col | Label column name. The column should be a numeric column. Usually this column is output by |

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. |

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. |

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`

.

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.

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()`

if (FALSE) { 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 %>% ml_gradient_boosted_trees(Sepal_Length ~ Petal_Length + Petal_Width) pred <- ml_predict(gbt_model, iris_test) ml_regression_evaluator(pred, label_col = "Sepal_Length") }