![]() ![]() We have to choose this function from a set of potential functions that can serve the need. We can think of supervised learning as choosing a function that achieves a given goal. And hope that this sample dataset is representative of the whole population. We select a small number of cats and dogs to build our dataset. Even though it would be a fun activity (no doubt!), it wouldn't be practical! So what do we do? We use the next best thing available to us - a training dataset that's representative of the classes. To build a truly accurate model, we need to build a dataset of images of all the cats and dogs in the world. If you show an image of a white cat, it will classify it as a dog. If we select 100 random images where cats are all black and dogs are all white, then the model will incorrectly assume that color is the differentiating feature between cats and dogs. ![]() For example, let's say you want to build a model that can look at an image and tell us if it's a cat or a dog. When it comes to solving supervised learning problems, we don't have access to every single data point that represents each class in its entirety. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |