[SYSTEMDS-986] Add mllearn and scala wrappers for random forest

• Type: Task
• Status: Closed
• Priority: Major
• Resolution: Invalid
• Affects Version/s: SystemDS 3.1
• Fix Version/s: Not Applicable
• Component/s: APIs
• Labels: Hacktoberfest starter
• Assignee: Sandeep Narayanaswami
• Reporter: Niketan Pansare
• Created: 29/Sep/16 18:55
• Updated: 15/Mar/23 19:14
• Resolved: 18/Jun/22 08:16

Description

See https://apache.github.io/incubator-systemml/algorithms-classification.html#random-forests for usage.

Since this is a starter task, I describe the steps to complete this task:
1. Implement a scala class (which inherits from BaseSystemMLClassifier) similar to https://github.com/apache/incubator-systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/NaiveBayes.scala
2. Modify getTrainingScript and getPredictionScript to specify the parameters used. See the algorithm documentation for these parameters.
3. Ensure that you implement appropriate traits to accept hyperparameters (eg: HasLaplace, HasIcpt, HasRegParam, HasTol, etc). These traits are available at https://github.com/apache/incubator-systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala#L36
4. Implement a python class (that extends BaseSystemMLClassifier) with constructor similar to https://github.com/apache/incubator-systemml/blob/master/src/main/python/systemml/mllearn/estimators.py#L284 which essentially accepts the hyperparameters and invokes the scala side methods (example: self.estimator.setLaplace(laplace))
5. Update the algorithm documentation by specifying the usage as well as examples.