我尝试在代码中使用 KNN 回归模型，但它显示属性错误。逻辑回归和决策树分类器效果很好。
这是代码
sklearn.neighbors import KNeighborsClassifier
KNN = KNeighborsClassifier().fit(X_train,Y_train)
train_preds5 = KNN.predict(X_train)
print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds5))
这是错误
AttributeError Traceback (most recent call last)
Cell In[87], line 5
3 KNN = KNeighborsClassifier().fit(X_train,Y_train)
4 #predict on train
----> 5 train_preds5 = KNN.predict(X_train)
6 #accuracy on train
7 print("Model accuracy on train is: ", accuracy_score(Y_train, train_preds5))
File ~\anaconda3\Lib\site-packages\sklearn\neighbors\_classification.py:234, in KNeighborsClassifier.predict(self, X)
218 """Predict the class labels for the provided data.
219
220 Parameters
(...)
229 Class labels for each data sample.
230 """
231 if self.weights == "uniform":
232 # In that case, we do not need the distances to perform
233 # the weighting so we do not compute them.
--> 234 neigh_ind = self.kneighbors(X, return_distance=False)
235 neigh_dist = None
236 else:
File ~\anaconda3\Lib\site-packages\sklearn\neighbors\_base.py:824, in KNeighborsMixin.kneighbors(self, X, n_neighbors, return_distance)
817 use_pairwise_distances_reductions = (
818 self._fit_method == "brute"
819 and ArgKmin.is_usable_for(
820 X if X is not None else self._fit_X, self._fit_X, self.effective_metric_
821 )
822 )
823 if use_pairwise_distances_reductions:
--> 824 results = ArgKmin.compute(
825 X=X,
826 Y=self._fit_X,
827 k=n_neighbors,
828 metric=self.effective_metric_,
829 metric_kwargs=self.effective_metric_params_,
830 strategy="auto",
831 return_distance=return_distance,
832 )
834 elif (
835 self._fit_method == "brute" and self.metric == "precomputed" and issparse(X)
836 ):
837 results = _kneighbors_from_graph(
838 X, n_neighbors=n_neighbors, return_distance=return_distance
839 )
File ~\anaconda3\Lib\site-packages\sklearn\metrics\_pairwise_distances_reduction\_dispatcher.py:277, in ArgKmin.compute(cls, X, Y, k, metric, chunk_size, metric_kwargs, strategy, return_distance)
196 """Compute the argkmin reduction.
197
198 Parameters
(...)
274 returns.
275 """
276 if X.dtype == Y.dtype == np.float64:
--> 277 return ArgKmin64.compute(
278 X=X,
279 Y=Y,
280 k=k,
281 metric=metric,
282 chunk_size=chunk_size,
283 metric_kwargs=metric_kwargs,
284 strategy=strategy,
285 return_distance=return_distance,
286 )
288 if X.dtype == Y.dtype == np.float32:
289 return ArgKmin32.compute(
290 X=X,
291 Y=Y,
(...)
297 return_distance=return_distance,
298 )
File sklearn\metrics\_pairwise_distances_reduction\_argkmin.pyx:95, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute()
File ~\anaconda3\Lib\site-packages\sklearn\utils\fixes.py:139, in threadpool_limits(limits, user_api)
137 return controller.limit(limits=limits, user_api=user_api)
138 else:
--> 139 return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:171, in threadpool_limits.__init__(self, limits, user_api)
167 def __init__(self, limits=None, user_api=None):
168 self._limits, self._user_api, self._prefixes = \
169 self._check_params(limits, user_api)
--> 171 self._original_info = self._set_threadpool_limits()
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:268, in threadpool_limits._set_threadpool_limits(self)
265 if self._limits is None:
266 return None
--> 268 modules = _ThreadpoolInfo(prefixes=self._prefixes,
269 user_api=self._user_api)
270 for module in modules:
271 # self._limits is a dict {key: num_threads} where key is either
272 # a prefix or a user_api. If a module matches both, the limit
273 # corresponding to the prefix is chosed.
274 if module.prefix in self._limits:
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:340, in _ThreadpoolInfo.__init__(self, user_api, prefixes, modules)
337 self.user_api = [] if user_api is None else user_api
339 self.modules = []
--> 340 self._load_modules()
341 self._warn_if_incompatible_openmp()
342 else:
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:373, in _ThreadpoolInfo._load_modules(self)
371 self._find_modules_with_dyld()
372 elif sys.platform == "win32":
--> 373 self._find_modules_with_enum_process_module_ex()
374 else:
375 self._find_modules_with_dl_iterate_phdr()
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:485, in _ThreadpoolInfo._find_modules_with_enum_process_module_ex(self)
482 filepath = buf.value
484 # Store the module if it is supported and selected
--> 485 self._make_module_from_path(filepath)
486 finally:
487 kernel_32.CloseHandle(h_process)
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:515, in _ThreadpoolInfo._make_module_from_path(self, filepath)
513 if prefix in self.prefixes or user_api in self.user_api:
514 module_class = globals()[module_class]
--> 515 module = module_class(filepath, prefix, user_api, internal_api)
516 self.modules.append(module)
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:606, in _Module.__init__(self, filepath, prefix, user_api, internal_api)
604 self.internal_api = internal_api
605 self._dynlib = ctypes.CDLL(filepath, mode=_RTLD_NOLOAD)
--> 606 self.version = self.get_version()
607 self.num_threads = self.get_num_threads()
608 self._get_extra_info()
File ~\anaconda3\Lib\site-packages\threadpoolctl.py:646, in _OpenBLASModule.get_version(self)
643 get_config = getattr(self._dynlib, "openblas_get_config",
644 lambda: None)
645 get_config.restype = ctypes.c_char_p
--> 646 config = get_config().split()
647 if config[0] == b"OpenBLAS":
648 return config[1].decode("utf-8")
AttributeError: 'NoneType' object has no attribute 'split'
我尝试导入 KNN 回归模型并使用它执行预测并检查我正在使用的数据集的准确性。 我本以为它会显示准确度分数，但它却显示属性错误。
尝试更改为： 您想要实例化一个 KNeighborsClassifier 对象，然后在单独的行中调用 X_train 和 y_train 上的 fit 方法。 最后，您可以使用拟合模型来生成预测。
from sklearn.neighbors import KNeighborsClassifier
KNN = KNeighborsClassifier()
KNN.fit(X_train,Y_train)
train_preds5 = KNN.predict(X_train)