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Data Science with R: Decision Trees and Random Forests

Forms of Supervised Learning

Explore the two core forms of supervised learning: classification and regression. Understand how categorical and numeric labels influence model creation and follow the typical workflow from data preparation to model training, using practical examples from datasets. Gain foundational knowledge applicable to various machine learning algorithms in data science.

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The two forms of supervised learning

Conceptually, supervised learning is divided into two forms based on the nature of the label used to produce the machine learning model: classification and regression.

Classification

When the label data is categorical, the supervised learning process produces a classification machine learning model.

Classification is arguably the most common scenario where data scientists apply machine learning. Classification problems exist across all types of organizations. Here are some examples:

  • Fraud detection

  • Churn prevention

  • Predicting patient admittance at a hospital

  • Nonprofit donor conversion

Given the importance of classification in data science, this course focuses on classification as the teaching vehicle for machine ...