In Random Forest Regressors, what is a difference between SKlearn and Spark ML parameters?

In the context of Random Forest Regressors, there are several key parameters that are specified differently in Scikit-Learn (SKlearn) and Spark ML. Understanding these differences is crucial for effectively using these libraries for machine learning tasks.

The parameter n_estimators in SKlearn, which denotes the number of trees in the forest, is indeed equivalent to numTrees in Spark ML. This indicates that both libraries allow you to set the number of trees to ensemble but use different naming conventions.

Similarly, max_depth in SKlearn, which limits the maximum depth of each tree, corresponds to maxDepth in Spark ML. This parameter is essential for controlling the complexity of the trees and preventing overfitting.

Furthermore, the parameter max_features in SKlearn determines the number of features to consider for the best split at a node, while in Spark ML, it is represented by featureSubsetStrategy. This difference highlights how the implementation of feature selection can vary between libraries.

Since all these parameters have corresponding equivalents in the respective libraries but with different names, it confirms that the answer includes all of the stated differences between the two frameworks. Recognizing these distinctions can help practitioners effectively transition between using SKlearn and Spark ML for