Decision Tree is a very common classification method. It is based on the probability of occurrence of various situations. By constructing a decision tree to obtain the probability that the expected value of the net present value is greater than or equal to zero, in order to evaluate project risk and its feasibility. In machine learning, Decision Tree is a predictive model that represents a mapping between object properties and object values. Entropy=The degree of clutter in the system, using the algorithm ID3, C4.5 and C5.0 spanning tree algorithms using entropy. This course introduces the basic concepts of Decision Tree, algorithms and how to build decision tree from Python's Scikit-learn library.
This training is for people who have Python Programming experience and want to learn machine learning skills
- Certification:Apsara Clouder - Big Data Specialist Certification:Implement Decision Tree with Python
- Duration:30 Minutes
- Available Languages:English
- Valid till：Mar 12, 2019
- Registration Fee:USD 1.00, Original Price:USD 10.00
Implement Decision Tree with Python
Through this course, you will learn what is Decision Tree, three algorithms for decision trees, and how to build a decision tree with Python's Scikit-learn library.
- Decision Tree Overview
- Decision Tree Algorithms
- An Example of Decision Tree