A ネガティブサンプル refers to a data point in 機械学習 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 二値分類, 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.
一部の文脈では、 ネガティブサンプリング 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 不均衡なデータセット, where the number of positive samples is significantly lower than the number of negative samples.
全体として、ネガティブサンプルは機械学習モデルの学習過程を導き、関連するインスタンスと無関係なインスタンスを効果的に区別できるようにする上で重要な役割を果たします。