📚 ML Regression Models

📖 Syllabus Dot Point

Design, develop and apply ML regression models using an OOP to predict numeric values
Including: linear regression, polynomial regression, logistic regression

In machine learning, a regression model learns a relationship between input data and output data. In many cases, the output is a numeric value, such as a future price, a test mark, the expected temperature, or the amount of rainfall. A strong student explanation should identify what the model predicts, how it fits a pattern, and why that pattern suits a particular task.

This lesson builds understanding step by step. First, students learn the general idea of regression models. Then they study three important models: linear regression, polynomial regression and logistic regression. Although logistic regression has the word regression in its name, students must notice that it is commonly used for classification by estimating probability and applying a threshold.

When software engineers design and develop these models using an object-oriented programming (OOP) approach, they organise the code into classes, methods and reusable structures. This makes the model easier to test, reuse and maintain. In this lesson, you will learn both the mathematics of the models and the programming design behind them.

⚠️ Important idea

Do not memorise only the model names. Learn the characteristics of each model. That means knowing the type of relationship it fits, what kind of output it gives, and what sort of problem it is best for.