```
library(sparklyr)
<- spark_connect(master = "local")
sc <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
iris_tbl
<- iris_tbl %>%
partitions sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
<- partitions$training
iris_training <- partitions$test
iris_test
<- iris_training %>%
dt_model ml_decision_tree(Species ~ .)
<- ml_predict(dt_model, iris_test)
pred
ml_multiclass_classification_evaluator(pred)
#> [1] 0.9393939
```

# Spark ML – Decision Trees

*R/ml_classification_decision_tree_classifier.R,*

## ml_decision_tree_classifier

## Description

Perform classification and regression using decision trees.

## Usage

```
ml_decision_tree_classifier(
x, formula = NULL,
max_depth = 5,
max_bins = 32,
min_instances_per_node = 1,
min_info_gain = 0,
impurity = "gini",
seed = NULL,
thresholds = NULL,
cache_node_ids = FALSE,
checkpoint_interval = 10,
max_memory_in_mb = 256,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("decision_tree_classifier_"),
...
)
ml_decision_tree(
x, formula = NULL,
type = c("auto", "regression", "classification"),
features_col = "features",
label_col = "label",
prediction_col = "prediction",
variance_col = NULL,
probability_col = "probability",
raw_prediction_col = "rawPrediction",
checkpoint_interval = 10L,
impurity = "auto",
max_bins = 32L,
max_depth = 5L,
min_info_gain = 0,
min_instances_per_node = 1L,
seed = NULL,
thresholds = NULL,
cache_node_ids = FALSE,
max_memory_in_mb = 256L,
uid = random_string("decision_tree_"),
response = NULL,
features = NULL,
...
)
ml_decision_tree_regressor(
x, formula = NULL,
max_depth = 5,
max_bins = 32,
min_instances_per_node = 1,
min_info_gain = 0,
impurity = "variance",
seed = NULL,
cache_node_ids = FALSE,
checkpoint_interval = 10,
max_memory_in_mb = 256,
variance_col = NULL,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
uid = random_string("decision_tree_regressor_"),
... )
```

## 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. |

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

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_instances_per_node | Minimum number of instances each child must have after split. |

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

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. |

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. |

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` . |

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. |

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. |

variance_col | (Optional) Column name for the biased sample variance of prediction. |

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. `ml_decision_tree`

is a wrapper around `ml_decision_tree_regressor.tbl_spark`

and `ml_decision_tree_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`

.

## Examples

## See Also

See https://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_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()`

, `ml_random_forest_classifier()`