Machine Learning and AI Foundations: Decision Trees with KNIME [Updated: 7/22/2025]

Suggested prerequisites

• General familiarity with supervised machine learning
• Understanding of terms such as target variable, input variable, algorithm, and train/test partition

Decision trees are transparent, available in every platform, and foundational to more advanced techniques like Random Forests and XGBoost. And if you’re a data scientist looking to pivot to machine learning, there’s arguably no better topic to kick off your learning journey. In this course, learn the essentials of machine learning pertaining to predictive analytics and working with decision trees. Along the way, instructor Keith McCormick provides demonstrations using the KNIME Analytics Platform, so you can grasp how these concepts work in real-world scenarios.

Learning objectives

• Understand how decision trees work by viewing demonstrations using the KNIME Analytics Platform.
• Build a decision tree using the KNIME Decision Tree Learner (C4.5).
• Understand how C54.5 handles missing data, nominal variables, and continuous variables.
• Master the Classification and Regression Tree (CART) algorithm which can be used with any machine learning platform, including R and Python.
• Using a regression tree to predict the value of a numerical target column.
• Evaluate the accuracy of a regression tree, a CART tree, and a C4.5 tree.