MLJS05 - Decision Trees, Ensemble learning and Random Forests analyses with Azure Machine Learning Service

Outline

• Construct  decision-tree for training and visualization, followed by bagging, pasting and Out-of-Bag evaluation methods to improve decision-tree predictions of personalized dataset: (a) zoo classification; and (b) mushrooms classification
• Construct  random forest (ensemble of decision trees), followed by Ada-Boosting and Gradient Boosting methods to improve decision-tree predictions of personalized dataset: (a) zoo classification; and (b) mushrooms classification
• Perform ensemble learning of the different predictors (SVM, decision-tree, classifiers)

Requirements

• Basic knowledge of Python Programming
• Participants who are joining this series for the first-time should refer to this document
• Participants will need to bring their own laptops

Mode of Training

Classroom

Workshop Format

Lecture - 1 hr | Hands-on tutorial - 1.25 hr | Q & A – 0.5 hr

Trainer

Alvin Chew is currently a Microsoft Cloud Research Software Fellow and a final-year PhD candidate in School of Civil and Environmental Engineering, Nanyang Technological University.