In supervised learning, we have two important parts: training vectors and testing vectors.
Training Vectors: These are collections of data with labels that help teach the model. For example, if we want the model to recognize cats, we use pictures of cats that are marked as "cat." This helps the model learn what a cat looks like.
Testing Vectors: After we finish training the model, we need to see how well it performs with new data. This means we show it pictures it hasn’t seen before to check if it can still identify cats correctly.
By splitting the data into training and testing, we make sure the model can work well with new information!
In supervised learning, we have two important parts: training vectors and testing vectors.
Training Vectors: These are collections of data with labels that help teach the model. For example, if we want the model to recognize cats, we use pictures of cats that are marked as "cat." This helps the model learn what a cat looks like.
Testing Vectors: After we finish training the model, we need to see how well it performs with new data. This means we show it pictures it hasn’t seen before to check if it can still identify cats correctly.
By splitting the data into training and testing, we make sure the model can work well with new information!