AI/ML BootCamp - Batch 01 - Session 06

COMSATS University Islamabad - Abbottabad, Pakistan

Welcome to Session 06 of the AI/ML BootCamp - Batch 01! In this session, participants will explore Decision Trees, a versatile machine learning algorithm used for classification and regression tasks. Through interactive lectures and practical exercises, attendees will learn how to build, visualize, and evaluate Decision Trees, enhancing their understanding of model interpretability and decision-ma

Dec 2, 2023, 4:00 – 5:00 PM

35 RSVP'd


Key Themes

Explore MLGeminiGoogle CloudKaggleML Study JamMachine LearningOpen SourceSolution ChallengeTensorFlow / Keras

About this event

Session 06 of the AI/ML BootCamp - Batch 01 is an exciting step forward in your machine learning journey, focusing on Decision Trees, one of the most intuitive and interpretable machine learning algorithms. This session will provide participants with a comprehensive understanding of how Decision Trees work, their applications, and the techniques for building and optimizing them.

Introduction to Decision Trees

The session will begin with an introduction to Decision Trees, explaining their structure and how they are used to make decisions. Participants will learn about the basic components of a Decision Tree, including nodes, branches, and leaves, and how these components represent decision points, possible outcomes, and final decisions.

How Decision Trees Work

Participants will delve into the mechanics of Decision Trees, understanding how they split data based on feature values to create branches and leaves. The session will cover key concepts such as entropy, information gain, Gini impurity, and how these metrics are used to determine the best splits at each node.

Building Decision Trees

Through hands-on exercises, participants will learn how to build Decision Trees using popular Python libraries such as scikit-learn. They will explore different criteria for splitting nodes, handle continuous and categorical features, and implement both classification and regression trees.

Visualizing and Interpreting Decision Trees

Visualization is a crucial aspect of understanding and interpreting Decision Trees. Participants will learn how to visualize their trees using tools like Graphviz and interpret the visualizations to gain insights into the decision-making process of their models.

Pruning and Overfitting

One of the challenges with Decision Trees is their tendency to overfit the training data. The session will cover techniques for pruning Decision Trees, which involve removing parts of the tree that do not provide additional power in predicting target variables, thereby improving the model’s generalization to new data.

Evaluating Decision Trees

Participants will learn how to evaluate the performance of their Decision Trees using metrics such as accuracy, precision, recall, and the F1 score for classification trees, and mean squared error (MSE) for regression trees. The session will also include cross-validation techniques to ensure robust model evaluation.

Advanced Topics

The session will also touch upon advanced topics such as ensemble methods that build on Decision Trees, including Random Forests and Gradient Boosting Trees. These methods combine multiple Decision Trees to improve predictive performance and robustness.

Session Highlights:

Understand the structure and components of Decision Trees.

Learn the splitting criteria and how to build Decision Trees for classification and regression.

Visualize and interpret Decision Trees to gain insights into model decisions.

Implement pruning techniques to prevent overfitting.

Evaluate the performance of Decision Trees using various metrics.

Explore advanced ensemble methods based on Decision Trees.

Join us for Session 06 of the AI/ML BootCamp - Batch 01, and gain expertise in one of the most interpretable and widely used machine learning algorithms. Whether you're aiming to build your first Decision Tree model or refine your existing skills, this session will provide you with the essential knowledge and practical experience to excel.

Don't miss this opportunity to master Decision Trees and enhance your machine learning toolkit with the AI/ML BootCamp - Batch 01!


  • Rida Zainab


    AI/ML Ninja


  • Muhammad Raees Azam

    GDSC COMSATS Abbottabad


  • Rizwan Shah

    GDSC COMSATS Abbottabad



  • Muhammad Raees Azam

    GDSC Lead

  • Hashir Ahmad Khan

    Former General Secretary

  • Maha Babar

    Comsats University

    Co- Lead

  • Nayab Zahra

    COMSATS University Islamabad

    Industrial & PR GURU


    Comsat University Islamabad Abbottabad Campus

    Information Technology Guru

  • Ibrahim Mir

    Comsats university abbottabad

    General Secretary

  • Areeb Ajab

    C.U.I, Abbottabad Campus

    Android Ninja

  • Sara Iftikhar

    COMSATS University Islamabad, Abbottabad Camous.

    Graphics Ninja

  • Wania Khan

    COMSATS University Islamabad, Abbottabad Campus.

    Membership Ninja

  • Muneer Hasan

    Flutter Ninja

  • Rida Zainab

    AI/ML Ninja

  • Muhammad Awais Khan

    Comsat University Islamabad Abbottabad Campus

    Web Ninja

  • Maria Adil


    Documentation Ninja

  • Varisha Sajjad

    Comsats University Abbottabad

    Marketing Ninja (F)

  • Muhammad Hasnain

    Media Ninja

  • Muhammad Danyal

    Comsats Abbotabad

    Membership Ninja ( M )

  • Jawaid Aziz


    Marketing Ninja ( M )

  • Malik Imran

    Comsats university abbottabad

    Media Ninja

  • Mukaram Awan

    COMSATS University Abbottabad Campus

    Graphics Ninja ( M )

  • Saqib Dawar

    Inventory Ninja

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