Previous Page

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