Decision Tree Modeling Using R Certification Training

Learning Objectives - In this module, you will learn how to design the data for modelling

Topics - Historical window, Performance window, Decide performance window horizon using Vintage analysis, General precautions related to data design

Learning Objectives - In this module, you will learn how to ensure Data Sanity check and you will also learn to perform the necessary checks before modelling 

Topics - Data sanity check-Contents, View, Frequency Distribution, Means / Uni-variate, Categorical variable treatment, Missing value treatment guideline, capping guideline

Learning Objectives - In this module, you will learn to use R and the Algorithm to develop the Decision Tree. 

Topics - Preamble to data, Installing R package and R studio, Developing first Decision Tree in R studio, Find strength of the model, Algorithm behind Decision Tree, How is a Decision Tree developed?, First on Categorical dependent variable, GINI Method, Steps taken by software programs to learn the classification (develop the tree), Assignment on decision tree

Learning Objectives - In this module you will understand how Classification trees are Developed, Validated and Used in the industry 

Topics - Discussion on assignment, Find Strength of the model, Steps taken by software program to implement the learning on unseen data, learning more from practical point of view, Model Validation and Deployment.

Learning Objectives - In this module you will understand the Advance stopping criteria of a decision tree. You will also learn to develop Decision Trees for numerous outcomes.

Topics - Introduction to Pruning, Steps of Pruning, Logic of pruning, Understand K fold validation for model, Implement Auto Pruning using R, Develop Regression Tree, Interpret the output, How it is different from Linear Regression, Advantages and Disadvantages over Linear Regression, Another Regression Tree using R

Learning Objectives - In this module you will learn what is Chi square and CHAID and their working and also the difference between CHAID and CART etc.. 

Topics - Key features of CART, Chi square statistics, Implement Chi square for decision tree development, Syntax for CHAID using R, and CHAID vs CART.

Learning Objectives - In this module you will learn about ID3, Entropy, Random Forest and Random Forest using R 

Topics - Entropy in the context of decision tree, ID3, Random Forest Method and Using R for Random forest method, Project work