Decision Tree and Classification Analysis

Decision Tree and Classification Analysis

Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description
This course covers key concepts regarding decision tree analysis and classification analysis using Microsoft R. Explore functions and techniques, including rxPredict, rxDForest, rxOneClassSvm, neural networks, and support vector machines.

Target Audience
All individuals who wish to understand key concepts in big data analysis and Microsoft R features including scientists, analysts, and statisticians

Prerequisites
None

Expected Duration (hours)
1.0

Lesson Objectives

Decision Tree and Classification Analysis

start the course

recognize essentials of classification algorithms

identify important machine learning algorithms that are available in Microsoft R

describe the Bayes’ Theorem and Naive Bayes classifier

recognize essentials of Support Vector Machines

describe rxOneClassSvm function and its important arguments

identify important aspects of One-class Support Vector Machines

describe regression tree analysis and its use cases

describe classification tree analysis and its use cases

describe rxDTree function and its important arguments

identify options for visualization of decision trees in Microsoft R

identify key features of rxDTree function

Course Number:
df_abdr_a08_it_enus

Expertise Level
Intermediate