Question

我正在尝试运行KNN回归，但始终出现以下错误：

ValueError: Query data dimension must match training data dimension

在模型中，我从数据中传入了5个特征作为训练集X。训练集的形状为（348,5）。我在该网站上看到了针对类似问题的其他一些答案，并尝试操纵预测数组Y_ = knn.predict（T）的形状，因为这是我认为我的问题所在，但是没有成功。感谢任何可以帮助我理解和解决我的问题的人。提前致谢。

模型：

knn = neighbors.KNeighborsRegressor(n_neighbors=10)
#training data:
    X = df[['PopSquared', 'IndicateBurglary', 'IndicateLarceny', 'IndicateGTA', '   IndicateArson']]

    Y = df['Property\ncrime'].values.reshape(-1, 1)

    knn.fit(X, Y)


# Set up prediction line.
T = np.arange(0, 50, 0.1)[:, np.newaxis]


Y_ = knn.predict(T)

完整的追溯错误消息：

ValueError                                Traceback (most recent call last)
<ipython-input-50-f35bfb134dc9> in <module>()
     12 
     13 # Trailing underscores are a common convention for a prediction.
---> 14 Y_ = knn.predict(T)

~\Anaconda3\lib\site-packages\sklearn\neighbors\regression.py in predict(self, X)
    142         X = check_array(X, accept_sparse='csr')
    143 
--> 144         neigh_dist, neigh_ind = self.kneighbors(X)
    145 
    146         weights = _get_weights(neigh_dist, self.weights)

~\Anaconda3\lib\site-packages\sklearn\neighbors\base.py in kneighbors(self, X, n_neighbors, return_distance)
    383                 delayed(self._tree.query, check_pickle=False)(
    384                     X[s], n_neighbors, return_distance)
--> 385                 for s in gen_even_slices(X.shape[0], n_jobs)
    386             )
    387             if return_distance:

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
    777             # was dispatched. In particular this covers the edge
    778             # case of Parallel used with an exhausted iterator.
--> 779             while self.dispatch_one_batch(iterator):
    780                 self._iterating = True
    781             else:

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
    623                 return False
    624             else:
--> 625                 self._dispatch(tasks)
    626                 return True
    627 

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
    586         dispatch_timestamp = time.time()
    587         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588         job = self._backend.apply_async(batch, callback=cb)
    589         self._jobs.append(job)
    590 

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
    109     def apply_async(self, func, callback=None):
    110         """Schedule a func to be run"""
--> 111         result = ImmediateResult(func)
    112         if callback:
    113             callback(result)

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
    330         # Don't delay the application, to avoid keeping the input
    331         # arguments in memory
--> 332         self.results = batch()
    333 
    334     def get(self):

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
    129 
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132 
    133     def __len__(self):

~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
    129 
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132 
    133     def __len__(self):

sklearn/neighbors/binary_tree.pxi in sklearn.neighbors.kd_tree.BinaryTree.query()

ValueError: query data dimension must match training data dimension

KNN回归给我ValueError：查询数据维必须与训练数据维匹配

0 个答案: