Random Forest

A Random Forest is made of many decision trees. Each tree in the forest predicts a record, and each tree "votes" for the final answer of the forest.
The forest chooses the class having the most votes.

A decision tree is a simple algorithm which builds a decision tree. Each node of the decision tree includes a condition on one of the input features.

When "growing" (ie, training) the forest:

for each tree, a random sample of the training set is used;
for each decision point in the tree, a random subset of the input features is considered.

Random Forests generally provide good results, at the expense of "explainability" of the model.

Feature subset strategy

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

Impurity

Criterion used for information gain calculation

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 ≥ number of categories in any categorical feature.

Checkpoint interval

Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in the SparkContext.

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.

Minimum information gain

Minimum information gain for a split to be considered at a tree node.

Minimum instance per node

Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid.

Subsampling rate

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

Seed

Maximum memory (MB)

Allocated to histogram aggregation