Or an English to Spanish translator whatsapp phone number list who can only translate texts that have already been translated. In general, the working hypothesis of these systems is that the test data will be somewhat similar to the training whatsapp phone number list data, but not the same. For example, if we train a model to detect pneumonia in humans, the model will be used in other humans, but not in animals. Or if we train a system to whatsapp phone number list translate from Spanish to English, the test texts will be different from the training ones, but they will always be written in.
Spanish, and not in whatsapp phone number list French. In this case, It is clear that a system that has been learned using Spanish texts will not be able to generalize to French. Or would we ask a French interpreter to translate Mandarin However, there are variations between the training and testing data that may be more subtle than the change from Spanish to French or from humans to animals, but which still have a whatsapp phone number list devastating effect on the quality of predictions.
Let's reimagine the case whatsapp phone number list of the system to distinguish between images of dogs and cats, but with a small variation: our database is only composed of black dogs and white cats. In this case, the color of the animal will be an extremely useful characteristic to distinguish between the two classes. In fact, it will give whatsapp phone number list us a perfect prediction: if the predominant color of the animal's body is black, it will be a dog; and if it is white, it will be a cat. Now let's imagine that in our test set there is a subtle whatsapp phone number list difference: white dogs appear. What do you think will happen to the predictions about the white dogs.