A muestra negativa refers to a data point in aprendizaje automático that represents an instance of the class that the model is not trying to predict. In contrast to positive samples, which are instances of the target class, negative samples help in training models to distinguish between different classes effectively.
In classification tasks, especially clasificación binaria, the model is trained to recognize the positive class (also known as the target or positive sample) and differentiate it from negative samples. For example, if a model is being trained to identify cats in images, images that do not contain cats would be considered negative samples.
Utilizing negative samples is essential for creating a robust and accurate model, as they help to minimize false positives—situations where the model incorrectly identifies an instance as belonging to the positive class. Including a diverse set of negative samples during training can enhance the model’s ability to generalize and perform well on unseen data.
En algunos contextos, muestreo negativo techniques might be employed, where random negative samples are selected from a larger dataset to improve training efficiency. This approach is particularly useful in scenarios with conjuntos de datos desequilibrados, where the number of positive samples is significantly lower than the number of negative samples.
En general, las muestras negativas desempeñan un papel crucial en guiar el proceso de aprendizaje de los modelos de aprendizaje automático, asegurando que puedan diferenciar eficazmente entre instancias relevantes e irrelevantes.