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Getting Started with Decision Trees
Rating: 4.2 out of 5(35 ratings)
2,694 students

Getting Started with Decision Trees

Learn the basics of Decision Trees - a popular and powerful machine learning algorithm and implement them using Python
Created byAnalytics Vidhya
Last updated 7/2025
English
English [Auto],

What you'll learn

  • Basics of Decision Trees
  • How to Apply Decision Trees to build Machine Learning models
  • Building Decision Tree models in Python
  • How to improve and optimize your decision tree models

Course content

1 section • 10 lectures • 51m total length

  • Introduction to Decision Tree4:20

    Explore how a flowchart-like decision tree uses a root node, branch nodes, and leaf nodes to split data and reveal non-linear patterns for classification.

  • Quiz: Introduction to Decision Trees
  • Let’s Visualize The Decision Tree7:52

    Visualize a decision tree built from a small subset of categorical data, and learn how splits, root and leaf nodes, and color-coded outcomes form a cart classifier.

  • How Do Decision Trees Decide7:29

    Examine how decision trees decide splits using gini impurity and information gain, and perform gini gain calculations on root and child nodes in a notebook.

  • How Decision Trees Make Predictions3:34

    Learn how a decision tree predicts unseen data by tracing root and subsequent node conditions, left and right branches, and leaf predictions, with probability estimates and reliability considerations.

  • Hands on Building the Decision Tree Classification Model- Part 111:36

    Build a decision tree classifier to predict high sales (unit sold > 1000) using engineered features, label encoding, and train-test evaluation, then assess overfitting and pruning strategies.

  • Hyperparameters of Decision Trees5:41

    Learn how decision tree hyperparameters like criterion (gini, entropy, logarithmic loss) and splitter shape model performance. Discover pruning, max depth, and min samples to combat overfitting.

  • Hands on Building the Decision Tree Classification Model - Part 22:59

    Tune a decision tree with maxdepth pruning and compare train and test F1 scores across depths while exploring minsamplesleaf and preparing for the next regression course.

  • Handling Imbalanced Datasets - Hands on7:25

    Master strategies for imbalanced datasets in decision trees by using stratified splits, class weights, and metrics like F1, precision, and recall; balance data with undersampling and oversampling, including SMOTE.

  • Reading: Working with Imbalanced Datasets0:05
  • Bonus Lecture0:49
  • Quiz: Decision Trees

Requirements

  • This course requires you to know basic Machine Learning algorithms like Linear Regression, Logistic Regression
  • Familiarity with Python would be an advantage

Description

Decision Tree algorithm is one of the most powerful algorithms in machine learning and data science. It is very commonly used by data scientists and machine learning engineers to solve business problem and explain that to your customers easily. This course will introduce you to the concept of Decision Trees and teach you how to build one using Python


Why learn about Decision Trees?

  • Decision Trees are the most widely and commonly used machine learning algorithms.

  • It can be used for solving both classification as well as regression problems.

  • Decision Trees are easy to interpret and hence have multiple applications around different industries.

What would you learn in Getting started with Decision Tree course?

  • Introduction to Decision Trees

  • Terminologies related to decision trees

  • Different splitting criterion for decision tree like Gini, chi-square, etc.

  • Implementation of decision tree in Python

Who this course is for:

  • Beginner Data Science Learners
  • Python Developers

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