Which parameter is unique to Spark ML for Random Forest algorithms?

The parameter that is unique to Spark ML for Random Forest algorithms is maxBins. This parameter is specific to the way that Spark ML processes and optimizes data in the context of decision trees and ensemble methods like Random Forests. In Spark ML, maxBins defines the maximum number of bins into which the continuous features are divided when building the trees. This binning process helps in reducing the complexity of the data and aids in efficient memory utilization during the model training, which is particularly critical for large datasets typically handled in Spark environments.

On the other hand, parameters like n_estimators, max_depth, and max_features are commonly found across various machine learning libraries, such as Scikit-learn and others. N_estimators refers to the number of trees in the forest, max_depth controls how deep each individual tree can grow, and max_features determines the fraction of features to consider when looking for the best split. These are standard hyperparameters that exist in many implementations of Random Forest algorithms, not specific to Spark ML.