This course teaches learners about decision tree algorithms, their types, and applications in real-world problem-solving. The course covers topics such as decision tree learning, splitting criteria, misclassification error, entropy, and probability. By the end of the course, students will be able to understand decision tree algorithms, their advantages, disadvantages, and how to implement them for classification purposes. The course is suitable for individuals interested in machine learning, data science, and analytics.
Decision Tree Algorithm Tutorial - Machine Learning and Example
Overview
Syllabus
- Introduction to decision tree.
- What is decision tree learning and its types.
- Classification tree learning and important terms.
- Difference between tree model and logistic regression model.
- Decision Tree Case Study: Gold loan attrition problem.
- Career Fields in Analytics.
- Gini index, entropy,.
- Decision tree algorithms.
- Advantages and disadvantages of decision tree.
- Decision tree example.
- Summary.
Taught by
Great Learning
