In the $k-$hearest neighbors method, when the value of $k$ is set to $1q,$

the classification or prediction of a new observation is based solely on the single most similar observation from the training seet

the classification or prediction of a new observation is subject to the smallest possible classification ertor

the new observation's class is nalvely assigned to the most common class in the training set

the new observation's prediction is used to estimate the anticipated error rate on future data over the entire training set