Question

我正试图安装一个任意的森林树模型,但我却继续把一种错误推为一种错误,即我CV的探测器被叫作空,而情况并非如此。

下面是密码和错误。

%%time
rf_val.fit(X_train, y_train)

Fitting 0 folds for each of 32 candidates, totalling 0 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=-1)]: Done   0 out of   0 | elapsed:    0.0s finished
ValueError                                Traceback (most recent call last)
<timed eval> in <module>

/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
    708                 return results
    709 
--> 710             self._run_search(evaluate_candidates)
    711 
    712         # For multi-metric evaluation, store the best_index_, best_params_ and

/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)
   1149     def _run_search(self, evaluate_candidates):
   1150         """Search all candidates in param_grid"""
-> 1151         evaluate_candidates(ParameterGrid(self.param_grid))
   1152 
   1153 

/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params)
    690 
    691                 if len(out) < 1:
--> 692                     raise ValueError( No fits were performed.  
    693                                       Was the CV iterator empty?  
    694                                       Were there no candidates? )

ValueError: No fits were performed. Was the CV iterator empty? Were there no candidates?

我正试图调整和适应随机的森林模式,并期望它能够及时完成,但我遇到了这一错误,我可以 which。

Answer 1

在原有的舱面代码中,fit功能:

def evaluate_candidates(candidate_params):
    candidate_params = list(candidate_params)
    n_candidates = len(candidate_params)

    if self.verbose > 0:
        print("Fitting {0} folds for each of {1} candidates,"
              " totalling {2} fits".format(
                  n_splits, n_candidates, n_candidates * n_splits))

    out = parallel(delayed(_fit_and_score)(clone(base_estimator),
                                           X, y,
                                           train=train, test=test,
                                           parameters=parameters,
                                           **fit_and_score_kwargs)
                   for parameters, (train, test)
                   in product(candidate_params,
                              cv.split(X, y, groups)))

    if len(out) < 1:
        raise ValueError( No fits were performed.  
                          Was the CV iterator empty?  
                          Were there no candidates? )

在你的错误信息中:

Fitting 0 folds for each of 32 candidates, totalling 0 fits

这意味着n_split = 0。

<代码>n_split

 n_splits = cv.get_n_splits(X, y, groups)

您的<代码>group为None,因此该错误可能产生于您的投入数据X和。

友情链接