{"id":5060,"date":"2024-02-15T17:01:27","date_gmt":"2024-02-15T17:01:27","guid":{"rendered":"https:\/\/dalelane.co.uk\/blog\/?p=5060"},"modified":"2026-04-02T18:26:33","modified_gmt":"2026-04-02T18:26:33","slug":"explaining-regression-in-scratch","status":"publish","type":"post","link":"https:\/\/dalelane.co.uk\/blog\/?p=5060","title":{"rendered":"Explaining regression in Scratch"},"content":{"rendered":"

In this post, I want to share a preview of a new feature I’m adding to Machine Learning for Kids<\/a>, to ask for feedback and ideas for projects that it could be used to make.<\/strong><\/p>\n

I’ll start by contrasting this new feature with what I’ve done with Machine Learning for Kids<\/a> before, then I’ll share screenshots of the new feature, and finally I’ve got a ten-minute video showing the sort of school lesson that I think it could be used for.<\/p>\n

<\/p>\n

Machine Learning for Kids so far<\/h3>\n

Until now, most of the machine learning models I’ve enabled in Machine Learning for Kids<\/a> have been classification models. <\/p>\n

Students collect examples of different types of things, and create a machine learning model that can recognize what type a new thing is. <\/p>\n

Classifiers are simple to explain, easy to train, and enable a variety of interesting projects. <\/p>\n

Collect examples of headlines from different newspapers. Give the model a new headline, and it can recognize which newspaper the headline is from.<\/p>\n

<\/p>\n

Collect examples of statistics of Pokemon of different types. Give the model the stats for a new Pokemon, and it can recognize what type the Pokemon is.<\/p>\n

<\/p>\n

Collect examples of pictures from a traffic camera, sorted into the number of passengers that can be seen. Give the model a picture of a new car, and it can recognize whether the car belongs in the car pool lane.<\/p>\n

<\/p>\n

Collect examples of recordings of different types of sounds, sorted into different commands. Give the model a new sound recording, and it can recognize which command it has heard.<\/p>\n

<\/p>\n

Classification models.<\/p>\n

I’ve seen hundreds of projects created using this simple pattern. The input is collection of things, sorted into buckets. The output is the name of the bucket that a new thing should go into.<\/p>\n

Regression models can enable different project types<\/h3>\n

With regression models, you still collect training examples, but instead of sorting them into groups, each examples has one or more numerical output values.<\/p>\n

When you give the model a new example, instead of recognizing what bucket it should go into, the model predicts what the output value(s) should be.<\/p>\n

Example of a regression project<\/h3>\n

My first test\/demo of the regression model support in action is predicting where a bouncing ball will first hit the ground. The training examples are the starting location, speed, and direction. <\/p>\n

<\/p>\n

The model predicts the x coordinate for where the ball will first hit the ground – so you can use that to try and catch the ball before it hits the ground.<\/p>\n

<\/p>\n

This is a nice example of the sort of project that you couldn\u2019t create using classification models, so I think it\u2019s a nice extension to Machine Learning for Kids.<\/p>\n

Demo of the new feature in action<\/h3>\n