With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

The error message is indicating that with the current combination of parameters, there are not enough samples to create a non-empty training set.

To fix this error, you could adjust one or more of the parameters in the train_test_split() function call. For example, you could increase the value of n_samples to ensure more data points are available for splitting, or decrease the value of test_size to allocate a smaller proportion of the data to the test set. Alternatively, if you specify a value for train_size instead of using the default value of None, you can ensure that a non-empty training set is created.

相关问题

还是报错With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

这个错误提示表示你的数据集只有一个样本，并且测试集的大小为数据集大小的 20%，因此在这种情况下，生成的训练集是空的。这是因为不能将一个样本同时划分到训练集和测试集中。

为了解决这个问题，你需要调整训练集和测试集的大小，以确保每个集合至少有一个样本。如果你的数据集只有一个样本，那么你可以将整个数据集作为训练集或测试集。例如：

X_train, X_test, y_train, y_test = X, X, y, y

这里将整个数据集作为训练集和测试集，即将 `X` 复制到 `X_train` 和 `X_test` 中，将 `y` 复制到 `y_train` 和 `y_test` 中。

当然，如果你的数据集中有多个样本，你需要根据数据集的大小和分布来调整训练集和测试集的大小，以确保每个集合至少有一个样本。

With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

This error occurs because when `n_samples=0` and `test_size=0.2`, the number of samples in the test set will be 0, which means the number of samples in the training set will be equal to the total number of samples in the dataset. If `train_size=None` (which is the default), it will try to set the training set size to the remaining samples after the test set has been extracted, but since there are no remaining samples, the training set will be empty.

To resolve this issue, you need to adjust either `n_samples` or `test_size` to ensure that there are enough samples in both the training and test sets. You may also want to set `train_size` to a specific value to ensure that the training set is not empty. For example, you could set `n_samples=100`, `test_size=0.2`, and `train_size=0.8` to split the dataset into 80% training and 20% test sets with a total of 100 samples.