如何处理报错:plot_decision_regions() got an unexpected keyword argument 'res'
时间: 2023-11-19 20:04:02 浏览: 36
该报错提示plot_decision_regions()函数中有一个未预期的关键字参数'res'。这可能是由于您在调用该函数时使用了一个不存在的参数'res',或者您可能使用了一个不兼容的版本的库。
要解决这个问题,您可以尝试以下步骤:
1. 检查您的代码,确保您正确地使用了plot_decision_regions()函数,并且没有拼写错误或语法错误。
2. 确保您正在使用正确版本的库。如果您使用的库太旧或太新,可能会导致该问题。尝试更新您的库或使用与您的库版本兼容的代码。
3. 如果您仍然无法解决问题,请查看文档或库的源代码,以查看plot_decision_regions()函数的参数列表,并确保您正确地使用了所有参数。
相关问题
TypeError: plot_learning_curve() got an unexpected keyword argument 'figsize'
This error occurs when the `plot_learning_curve()` function is called with an additional argument `figsize`, which is not defined in the function. This means that the function does not accept the `figsize` argument, and it is causing a TypeError.
To resolve this error, you can remove the `figsize` argument from the function call or modify the `plot_learning_curve()` function to accept the `figsize` argument. If you want to modify the function, you can do so by adding the `figsize` argument to the function definition and using it to set the size of the plot in the function code.
For example:
```python
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5), figsize=None):
if figsize:
plt.figure(figsize=figsize)
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
plt.legend(loc="best")
return plt
```
Now, you can call the `plot_learning_curve()` function with the `figsize` argument:
```python
plot_learning_curve(estimator, 'Learning Curve', X_train, y_train, figsize=(8, 6))
```
TypeError: plot_partial_dependence() got an unexpected keyword argument 'ax'
这个错误通常发生在使用旧版本的Scikit-learn库调用plot_partial_dependence()函数时。在旧版本的Scikit-learn库中,plot_partial_dependence()不支持ax参数。
解决方法是升级Scikit-learn库到最新版本。您可以使用以下命令升级Scikit-learn库:
```
pip install --upgrade scikit-learn
```
如果您使用的是conda,则可以使用以下命令升级Scikit-learn库:
```
conda update scikit-learn
```
如果升级Scikit-learn库不起作用,请尝试删除所有Scikit-learn库并重新安装最新版本:
```
pip uninstall scikit-learn
pip install scikit-learn
```
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