me explicas qué es ovo y ovr en svm?

“Me explicas qué es ovo y ovr en svm?” means “Can you explain to me what ovo and ovr are in svm?” OVO and OVR are acronyms for two related approaches to the problem of multi-class classification in a Support Vector Machine (SVM). OVO stands for “One-vs-One” and is a strategy of decomposing the multi-class problem into multiple binary classification problems. This approach requires the creation of one classifier for each pair of classes. OVR stands for “One-vs-Rest” and is a strategy of decomposing the multi-class problem into multiple binary classification problems. This approach requires the creation of one classifier for each class, which is trained to identify that class versus all other classes. An example of OVO in a SVM could be a model that is trying to classify animals as either cats, dogs, or birds. In this case, the model would be trained using three binary classifiers: one to distinguish cats from dogs, another to distinguish cats from birds, and the last to distinguish dogs from birds. An example of OVR in a SVM could be the same model trying to classify animals as either cats, dogs, or birds. In this case, the model would be trained using three binary classifiers: one to distinguish cats from all other animals, another to distinguish dogs from all other animals, and the last to distinguish birds from all other animals. Fun Fact: SVMs are one of the most popular machine learning algorithms and are used in a variety of fields, including finance, medicine, and computer vision.