Random Forest Classifier

Creates a random forest classification model.

This operation is ported from Spark ML.

For a comprehensive introduction, see Spark documentation.

For scala docs details, see org.apache.spark.ml.classification.RandomForestClassifier documentation.

Since: Seahorse 1.1.0

Input

This operation does not take any input.

Output

Port	Type Qualifier	Description
`0`	`Estimator`	An `Estimator` that can be used in a Fit operation.

Parameters

Name	Type	Description
`max depth`	`Numeric`	The maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
`max bins`	`Numeric`	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. Must be >= 2 and >= number of categories in any categorical feature.
`min instances per node`	`Numeric`	The minimum number of instances each child must have after split. If a split causes the left or right child to have fewer instances than the parameter's value, the split will be discarded as invalid.
`min information gain`	`Numeric`	The minimum information gain for a split to be considered at a tree node.
`max memory`	`Numeric`	Maximum memory in MB allocated to histogram aggregation.
`cache node ids`	`Boolean`	The caching nodes IDs. Can speed up training of deeper trees.
`checkpoint interval`	`Numeric`	The checkpoint interval. E.g. 10 means that the cache will get checkpointed every 10 iterations.
`classification impurity`	`SingleChoice`	The criterion used for information gain calculation. Possible values: `["entropy", "gini"]`
`subsampling rate`	`Numeric`	The fraction of the training data used for learning each decision tree.
`seed`	`Numeric`	The random seed.
`num trees`	`Numeric`	The number of trees to train.
`feature subset strategy`	`SingleChoice`	The number of features to consider for splits at each tree node. Possible values: `["auto", "onethird", "sqrt", "log2"]`
`label column`	`SingleColumnSelector`	The label column for model fitting.
`features column`	`SingleColumnSelector`	The features column for model fitting.
`probability column`	`String`	The column for predicted class conditional probabilities.
`raw prediction column`	`String`	The raw prediction (confidence) column.
`prediction column`	`String`	The prediction column created during model scoring.