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Decision Tree

Learn decision tree from basics in this free online training. Decision tree course is taught hands-on by experts. Learn about introduction to decision tree along with examples of decision tree & lot more.

Instructor:

Prof. Mukesh Rao
4.38
average rating

Ratings

Beginner

Level

2.25 Hrs

Learning hours

3.1K+

Learners

Skills you’ll Learn

About this Course

A Decision Tree is a way of displaying an algorithm containing only conditional control statements. It uses a tree-like structure for decisions and their possible consequences including chance events. A Decision tree consists of Decision nodes represented in square type, Chance nodes typically represented by the circle, and Endnotes represented in triangles. They are most commonly used in operations research and operations management. We can also descriptively use the decision tree for calculating conditional probabilities. The decision tree algorithm fits in the category of supervised learning with the help of the algorithm we can solve regression and classification problems. The structure of the algorithm is of tree type in which each leaf node corresponds to a class label and the internal node of the tree represents the attributes. The discrete attributes are used in the decision tree for representing any Boolean function. The decision tree is simple to understand and interpret; it requires little data preparation but the cost of using the tree is logarithmic in the context of data points used for training the tree. It can handle both numerical and categorical data. It also performs well when assumptions are violated by the true model from where the data was generated.

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Course Outline

Introduction to Decision Tree
Entropy and Heterogeneity Concept
Shannon's Entropy Decision Tree
Examples of Decision Tree
Preventing Overfitting

Our course instructor

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Prof. Mukesh Rao

Director- Data Science

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116.6K+ Learners
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8 Courses
Prof. Mukesh Rao is an Adjunct Faculty at Great Lakes for Big Data and Machine Learning. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science. Mukesh has conducted over 100 corporate trainings. Data Science training covers all the stages of CRISP DM, tools and techniques used in each stage, machine learning algorithms and their application. Big Data training covers core Apache Hadoop technologies including HDFS, YARN, Map Reduce, PIG, HIVE, SQOOP, FLUME, SPARK and MongoDB.

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Decision Tree

2.25 Learning Hours . Beginner

Why upskill with us?

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700+ free courses
In-demand skills & tools
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Free life time Access