交叉验证留一法

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交叉验证留一法

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The k-fold cross-validation method is widely used in machine learning and statistical analysis to evaluate the performance of a model. One of its variations is the leave-one-out cross-validation method, also known as the jackknife method. The leave-one-out method works by leaving one observation out of the sample at a time, and then using the remaining observations to train the model, with the goal of predicting the left-out observation. This process is repeated for each observation in the sample, and the results are averaged to obtain an estimate of the model's performance. The leave-one-out method is often preferred over other cross-validation methods because it provides an unbiased estimate of the model's performance when the sample size is small.