Course Content

  • decision tree regressor position salary dataset

Course Content


In the shape of a tree structure, a decision tree constructs regression or classification models. It incrementally cuts down a dataset into smaller and smaller sections while also developing an associated decision tree. A tree with decision nodes and leaf nodes is the end result.

One of the most widely used and useful models for supervised learning is the Decision Tree. It can be used to tackle both regression and classification problems, albeit the latter is more widely utilised. There are three sorts of nodes in this tree-structured classifier.

The key distinction between the random forest method and the decision tree algorithm is that decision trees are graphs that depict all possible outcomes of a decision using a branching strategy. The random forest algorithm, on the other hand, produces a set of decision trees that work in accordance with the output.

Recommended Courses

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image
App QR Code Image
Code Scan or Download the app
Google Play Store
Apple App Store
598K+ Downloads
App Download Section Circle 1
4.57 Avg. Ratings
App Download Section Circle 2
15K+ Reviews
App Download Section Circle 3
  • Learn anywhere on the go
  • Get regular updates about your enrolled or new courses
  • Share content with your friends
  • Evaluate your progress through practice tests
  • No internet connection needed
  • Enroll for the webinar and join at the time of the webinar from anywhere