MODULE 1
CLASSIFICATION AND REGRESSION TREE
(CART): DECISION TREE

• Introduction to decision trees
• Interpreting a decision tree
• Comprehension – decision tree
  classification in Python
• Regression with decision trees

MODULE 2
THEORY OF DECISION TREE

• Introduction
• Concept Of Homogeneity
• Gini Index
• Entropy And Information Gain
• Comprehension – Information Gain
• Splitting By R-Squared

MODULE 3
DECISION TREE HYPER-PARAMETER
TUNING

• Building decision trees in Python
• Choosing tree hyperparameters in
  Python
• Comprehension – hyperparameters
• Tree truncation
• Advantages and disadvantages tree
  truncation

MODULE 4
RANDOM FOREST ENSEMBLE BAGGING
TECHNIQUE

• Introduction
• Ensembles
• Comprehension – ensembles
• Creating a random forest
• Comprehension – OOB (out-of-bag)
  error
• Random forests lab