Linear discriminant analysis

Linear discriminant analysis is a way that computers help you make decisions about telling things apart. It's a bit like how you can tell different fruits apart by looking at them - for example, an apple from a banana.

If you think about it, there are certain things that might make it easier to tell them apart. For example, apples are usually round and red, while bananas are long and yellow.

Computers use a similar process to tell things apart. They look at certain characteristics, called features, of the things they're trying to tell apart. For example, if a computer was trying to tell apart different types of fruit, it might look at things like the shape, color, and size of the fruit.

Linear discriminant analysis is a special method that computers use to figure out the best way to tell things apart based on these features. It basically tries to find the features that are most different between the different things you're trying to tell apart.

For example, if you were trying to tell apart apples and bananas, a computer might use linear discriminant analysis to figure out that the size and shape of the fruit are the most important characteristics for telling them apart.

Once the computer has figured out the best way to tell things apart, it can use that information to make new predictions. For example, if you showed the computer a picture of a fruit it had never seen before, it could use the features it had learned to predict what type of fruit it was.