Forms of Supervised Learning

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 learning fundamentals via the Adult Census Income and Titanic datasets.

Regression

When the label data is numeric, the supervised learning process produces a regression machine learning model.

Regression problems have a long history across all types of organizations. Here are some examples:

• Sales forecasting

• Length of patient hospital stays

• Customer lifetime value

• Marketing mix

Later, the course covers constructing regression machine learning models. All machine learning fundamentals learned in the context of classification 100 percent apply to regression.

The terminology used in this course

Data science practitioners commonly use the terms “classification” and “regression.” It should be noted that the aforementioned is a conceptual framework and is not technically correct.

For example, a statistician would correctly point out that regression is a family of predictive analytics techniques that can be used with categorical (e.g., logistic regression) and numeric (e.g., linear regression) labels.

Despite the lack of rigor of the above terminology, this course uses the language common in the data science community.

Supervised learning workflow

Supervised learning follows the same high-level workflow, whether the problem is classification or regression. The following image visually depicts the supervised learning workflow: