ml_random_forest_classifier {sparklyr} | R Documentation

## Spark ML – Random Forest

### Description

Perform classification and regression using random forests.

### Usage

    ml_random_forest_classifier(x, formula = NULL, num_trees = 20L,
      subsampling_rate = 1, max_depth = 5L, min_instances_per_node = 1L,
      feature_subset_strategy = "auto", impurity = "gini", min_info_gain = 0,
      max_bins = 32L, seed = NULL, thresholds = NULL,
      checkpoint_interval = 10L, cache_node_ids = FALSE,
      max_memory_in_mb = 256L, features_col = "features", label_col = "label",
      prediction_col = "prediction", probability_col = "probability",
      raw_prediction_col = "rawPrediction",
      uid = random_string("random_forest_classifier_"), ...)

    ml_random_forest(x, formula = NULL, type = c("auto", "regression",
      "classification"), features_col = "features", label_col = "label",
      prediction_col = "prediction", probability_col = "probability",
      raw_prediction_col = "rawPrediction", feature_subset_strategy = "auto",
      impurity = "auto", checkpoint_interval = 10L, max_bins = 32L,
      max_depth = 5L, num_trees = 20L, min_info_gain = 0,
      min_instances_per_node = 1L, subsampling_rate = 1, seed = NULL,
      thresholds = NULL, cache_node_ids = FALSE, max_memory_in_mb = 256L,
      uid = random_string("random_forest_"), response = NULL, features = NULL,
      ...)

    ml_random_forest_regressor(x, formula = NULL, num_trees = 20L,
      subsampling_rate = 1, max_depth = 5L, min_instances_per_node = 1L,
      feature_subset_strategy = "auto", impurity = "variance",
      min_info_gain = 0, max_bins = 32L, seed = NULL,
      checkpoint_interval = 10L, cache_node_ids = FALSE,
      max_memory_in_mb = 256L, features_col = "features", label_col = "label",
      prediction_col = "prediction",
      uid = random_string("random_forest_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.
`num_trees` | Number of trees to train (>= 1). If 1, then no bootstrapping is used. If > 1, then bootstrapping is done.
`subsampling_rate` | Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)
`max_depth` | Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.
`min_instances_per_node` | Minimum number of instances each child must have after split.
`feature_subset_strategy` | The number of features to consider for splits at each tree node. See details for options.
`impurity` | Criterion used for information gain calculation. Supported: "entropy" and "gini" (default) for classification and "variance" (default) for regression. For `ml_decision_tree`, setting `"auto"` will default to the appropriate criterion based on model type.
`min_info_gain` | Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.
`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.
`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_random_forest` is a wrapper around `ml_random_forest_regressor.tbl_spark` and `ml_random_forest_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_predictor` 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 Also

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_gbt_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`

### Examples

    ## Not run:
    sc <- spark_connect(master = "local")
    iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

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

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

    rf_model <- iris_training %>%
      ml_random_forest(Species ~ ., type = "classification")

    pred <- sdf_predict(iris_test, rf_model)

    ml_multiclass_classification_evaluator(pred)

    ## End(Not run)

[Package sparklyr version 0.8.4 Index]