我在尝试使用网格搜索为我的SVM找到最佳超参数时，按照以下方式操作：

from sklearn.model_selection import GridSearchCVparam_grid = {'coef0': [10, 5, 0.5, 0.001], 'C': [100, 50, 1, 0.001]}poly_svm_search = SVC(kernel="poly", degree="2")grid_search = GridSearchCV(poly_svm_search, param_grid, cv=5, scoring='f1')grid_search.fit(train_data, train_labels)

我得到了以下错误：

---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-72-dadf5782618c> in <module>      8 ----> 9 grid_search.fit(train_data, train_labels)~/.local/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)    720                 return results_container[0]    721 --> 722             self._run_search(evaluate_candidates)    723     724         results = results_container[0]~/.local/lib/python3.6/site-packages/sklearn/model_selection/_search.py in _run_search(self, evaluate_candidates)   1189     def _run_search(self, evaluate_candidates):   1190         """Search all candidates in param_grid"""-> 1191         evaluate_candidates(ParameterGrid(self.param_grid))   1192    1193 ~/.local/lib/python3.6/site-packages/sklearn/model_selection/_search.py in evaluate_candidates(candidate_params)    709                                for parameters, (train, test)    710                                in product(candidate_params,--> 711                                           cv.split(X, y, groups)))    712     713                 all_candidate_params.extend(candidate_params)~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)    981             # remaining jobs.    982             self._iterating = False--> 983             if self.dispatch_one_batch(iterator):    984                 self._iterating = self._original_iterator is not None    985 ~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)    823                 return False    824             else:--> 825                 self._dispatch(tasks)    826                 return True    827 ~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)    780         with self._lock:    781             job_idx = len(self._jobs)--> 782             job = self._backend.apply_async(batch, callback=cb)    783             # A job can complete so quickly than its callback is    784             # called before we get here, causing self._jobs to~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)    180     def apply_async(self, func, callback=None):    181         """Schedule a func to be run"""--> 182         result = ImmediateResult(func)    183         if callback:    184             callback(result)~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)    543         # Don't delay the application, to avoid keeping the input    544         # arguments in memory--> 545         self.results = batch()    546     547     def get(self):~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)    259         with parallel_backend(self._backend):    260             return [func(*args, **kwargs)--> 261                     for func, args, kwargs in self.items]    262     263     def __len__(self):~/.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)    259         with parallel_backend(self._backend):    260             return [func(*args, **kwargs)--> 261                     for func, args, kwargs in self.items]    262     263     def __len__(self):~/.local/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)    526             estimator.fit(X_train, **fit_params)    527         else:--> 528             estimator.fit(X_train, y_train, **fit_params)    529     530     except Exception as e:~/.local/lib/python3.6/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)    210     211         seed = rnd.randint(np.iinfo('i').max)--> 212         fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)    213         # see comment on the other call to np.iinfo in this file    214 ~/.local/lib/python3.6/site-packages/sklearn/svm/base.py in _sparse_fit(self, X, y, sample_weight, solver_type, kernel, random_seed)    291                 sample_weight, self.nu, self.cache_size, self.epsilon,    292                 int(self.shrinking), int(self.probability), self.max_iter,--> 293                 random_seed)    294     295         self._warn_from_fit_status()sklearn/svm/libsvm_sparse.pyx in sklearn.svm.libsvm_sparse.libsvm_sparse_train()TypeError: an integer is required

我的train_labels变量包含一个布尔值列表，所以我有一个二元分类问题。train_data是一个<class 'scipy.sparse.csr.csr_matrix'>，基本上包含了所有scaled和One-Hot encoded的特征。

我做错了什么？对我来说很难追踪这里的问题所在。提前感谢您的任何帮助 ;)。

回答：

当你使用以下代码初始化SVC时：

poly_svm_search = SVC(kernel="poly", degree="2")

由于degree参数周围的引号，你提供了一个字符串值。但根据文档，degree需要一个整数值。

degree : int, optional (default=3) 多项式核函数（’poly’）的次数。其他所有核函数忽略此参数。

所以你需要这样做：

poly_svm_search = SVC(kernel="poly", degree=2)

注意这里我没有使用引号。