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.
时间: 2024-06-05 14:11:19 浏览: 338
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.
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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:
```python
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.
The error message indicates that the combination of parameters you have specified will result in an empty training set.
When you set `train_size=None`, the training set will be automatically set to the complement of the test set, which is calculated as `1 - test_size`.
If you set `n_samples=0`, this means that there are no samples available to create a training set.
To fix this error, you can adjust the values of `n_samples`, `test_size`, and `train_size` until you have a valid combination. For example, you can set a non-zero value for `n_samples`, or adjust the values of `test_size` and `train_size` to ensure that there are enough samples for both sets.
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