when a model predicts the wrong class for a sample

the process of removing incorrect, inconsistent or irrelevant data to improve the quality of a data set before training a model

unwanted or random variations in data that don’t represent meaningful patterns

a sample that has been incorrectly classified

British English CC

Improving a model

Computing

This unit is designed to develop pupils programming skills in a new engaging context: physical computing, using the BBC micro:bit. They will get acquainted with the host of components built into the micro:bit, and write simple programs that use these components to interact with the physical world.

Machine learning using the micro:bit

it reduces bias in the model's predictions

What is the main benefit of training an AI model on diverse data?

Cleaning data enhances the {{}} of a machine learning model.

Match the following terms with their descriptions.

a data point that is significantly different from others

Adding a null class helps prevent {{}} in machine learning models

it ensures the model can recognise valid movements from diverse individuals

Why is it important to include data from people with different movement styles when training a model to classify movements?

A machine learning model can make incorrect predictions if the training data has {{}}.

A model’s {{}} score tells us how likely the model’s prediction is correct.

Match the problem with its potential cause in machine learning.

A classifier learns by analysing {{}} between samples and grouping them into classes.

adding more diverse and well-labelled training data

What is one way to improve a machine learning model’s accuracy?

a missing value that has no impact on model performance

a data point that does not follow the general pattern of the rest of the data

a data point that is identical to all others

Which of the following best describes an outlier in a data set?

Exit Quiz