x_undersampling=undersampling_data[['type','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']]报错,Traceback (most recent call last): File "C:\Users\14493\Desktop\pythonProject1\azhe.py", line 73, in <module> x_undersampling=undersampling_data[['type','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']] File "C:\Users\14493\Desktop\pythonProject1\venv\lib\site-packages\pandas\core\frame.py", line 3767, in __getitem__ indexer = self.columns._get_indexer_strict(key, "columns")[1] File "C:\Users\14493\Desktop\pythonProject1\venv\lib\site-packages\pandas\core\indexes\base.py", line 5876, in _get_indexer_strict self._raise_if_missing(keyarr, indexer, axis_name) File "C:\Users\14493\Desktop\pythonProject1\venv\lib\site-packages\pandas\core\indexes\base.py", line 5938, in _raise_if_missing raise KeyError(f"{not_found} not in index") KeyError: "['type'] not in index"
时间: 2023-08-28 09:17:30 浏览: 108
这个错误提示表明 `undersampling_data` 数据框中没有名为 `type` 的列,因此无法将其选择出来赋值给 `x_undersampling` 变量。你需要检查一下数据框中的列名是否正确,或者数据框中是否存在这些列。另外,你也可以尝试使用 `undersampling_data.loc[:, ['type','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']]` 来选择这些列,这样可以确保即使数据框中没有某些列,代码也能正常运行。
相关问题
Traceback (most recent call last): File "C:\Users\14493\Desktop\pythonProject1\azhe.py", line 73, in <module> x_undersampling=undersampling_data[['type','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']] File "C:\Users\14493\Desktop\pythonProject1\venv\lib\site-packages\pandas\core\frame.py", line 3767, in __getitem__ indexer = self.columns._get_indexer_strict(key, "columns")[1] File "C:\Users\14493\Desktop\pythonProject1\venv\lib\site-packages\pandas\core\indexes\base.py", line 5876, in _get_indexer_strict self._raise_if_missing(keyarr, indexer, axis_name) File "C:\Users\14493\Desktop\pythonProject1\venv\lib\site-packages\pandas\core\indexes\base.py", line 5938, in _raise_if_missing raise KeyError(f"{not_found} not in index") KeyError: "['type'] not in index"
这是一个 KeyError 错误,意味着你在尝试对一个不存在的列进行索引。在你的代码中,你试图使用 `undersampling_data[['type','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']]` 来索引一个名为 `type` 的列,但是 `undersampling_data` 数据框中不存在 `type` 这个列。你需要检查一下你的数据框中是否存在名为 `type` 的列,或者使用适当的列名来进行索引。
ks = range(1,100) inSampleScores = [] crossValidationScores = [] d = {} #key = k, value = cv accuracy rate for k in ks: k_clf = KNeighborsClassifier(k).fit(train_x, train_y) inSampleScores.append(k_clf.score(train_x, train_y)) scores = cross_val_score(k_clf, train_x, train_y, cv = 5) crossValidationScores.append(scores.mean()) d[k] = scores.mean()怎么解除n_splits=5 cannot be greater than the number of members in each class.的报错
这个错误通常是因为某些类别的样本数太少导致的。可以尝试做以下几个步骤来解决这个问题:
1. 检查数据集中每个类别的样本数量是否相等。如果不相等,可以考虑对样本数量较少的类别进行过采样(oversampling)或对样本数量较多的类别进行欠采样(undersampling)。
2. 检查是否有重复的样本。如果有,可以考虑将它们删除。
3. 尝试减少交叉验证的折数。将 n_splits 设置为一个较小的值,比如 3 或者 4,可以减少每个折中每个类别的样本数量。
4. 如果以上方法都无法解决问题,可以尝试使用 StratifiedKFold 进行交叉验证。它可以确保每个折中每个类别的样本数量相等。示例代码如下:
```
from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=5)
scores = cross_val_score(k_clf, train_x, train_y, cv=skf)
```
希望这些方法可以帮助你解决这个问题。
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