neg_log_likelihood() missing 1 required positional argument: 'sigma'
时间: 2023-10-21 12:05:38 浏览: 31
This error message suggests that you are calling the `neg_log_likelihood()` function without providing the required argument `sigma`.
`neg_log_likelihood()` is likely a custom-defined function that calculates the negative log likelihood of a model's predictions given the true target values and a standard deviation `sigma`. You need to provide the `sigma` value as an argument when calling the function. The exact syntax may depend on how the function is defined, but it should be something like:
```
neg_log_likelihood(true_targets, model_predictions, sigma)
```
Make sure to replace `true_targets`, `model_predictions`, and `sigma` with the appropriate variables or values in your code.
相关问题
['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'completeness_score', 'explained_variance', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted', 'matthews_corrcoef', 'max_error', 'mutual_info_score', 'neg_brier_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error', 'neg_mean_gamma_deviance', 'neg_mean_poisson_deviance', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'neg_negative_likelihood_ratio', 'neg_root_mean_squared_error', 'normalized_mutual_info_score', 'positive_likelihood_ratio', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'rand_score', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'roc_auc_ovo', 'roc_auc_ovo_weighted', 'roc_auc_ovr', 'roc_auc_ovr_weighted', 'top_k_accuracy', 'v_measure_score']哪些适合xgboost回归
对于XGBoost回归问题,以下指标适合用于评估模型性能:
- explained_variance:解释方差
- neg_mean_absolute_error:负均绝对误差
- neg_mean_squared_error:负均方误差
- neg_mean_squared_log_error:负对数均方误差
- neg_median_absolute_error:负中值绝对误差
- r2:R平方值
这些指标可以通过scikit-learn库中的相关函数来计算。例如,使用neg_mean_absolute_error函数计算负均绝对误差:
```python
from sklearn.metrics import mean_absolute_error
# 假设y_true是真实值,y_pred是模型预测值
mae = mean_absolute_error(y_true, y_pred)
neg_mae = -mae
```
你可以根据需要选择适当的指标进行评估。请注意,这些指标适用于回归问题,不适用于分类问题。
raise ValueError( ValueError: For evaluating multiple scores, use sklearn.model_selection.cross_validate instead. ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'completeness_score', 'explained_variance', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted', 'matthews_corrcoef', 'max_error', 'mutual_info_score', 'neg_brier_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error', 'neg_mean_gamma_deviance', 'neg_mean_poisson_deviance', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'neg_negative_likelihood_ratio', 'neg_root_mean_squared_error', 'normalized_mutual_info_score', 'positive_likelihood_ratio', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'rand_score', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'roc_auc_ovo', 'roc_auc_ovo_weighted', 'roc_auc_ovr', 'roc_auc_ovr_weighted', 'top_k_accuracy', 'v_measure_score'] was passed.
这个错误是因为你在调用某个函数时传入了多个评估指标,而该函数不支持同时对多个指标进行评估。建议使用 sklearn.model_selection.cross_validate 函数来对多个指标进行评估。你可以将评估指标作为参数传递给该函数。例如:
```python
from sklearn.model_selection import cross_validate
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(random_state=0)
clf = LogisticRegression(random_state=0)
scoring = ['accuracy', 'precision_macro', 'recall_macro']
scores = cross_validate(clf, X, y, scoring=scoring)
```
这样就可以同时对 accuracy、precision_macro 和 recall_macro 三个指标进行评估了。