---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
D:\Users\psalm\AppData\Local\Temp/ipykernel_41176/1088380155.py in <module>
      9 knn_regr.fit(x_train, y_train)
     10 print("Performance of kNN regressor")
---> 11 breast_cancer_y_pred = knn_regr.predict(x_test)
     12 print("Mean squared error: %.2f" % mean_squared_error(y_test, breast_cancer_y_pred))
     13 print("Coefficient of determination: %.2f" % r2_score(y_test, breast_cancer_y_pred))

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neighbors\_regression.py in predict(self, X)
    206         X = check_array(X, accept_sparse='csr')
    207
--> 208         neigh_dist, neigh_ind = self.kneighbors(X)
    209
    210         weights = _get_weights(neigh_dist, self.weights)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neighbors\_base.py in kneighbors(self, X, n_neighbors, return_distance)
    703                 kwds = self.effective_metric_params_
    704
--> 705             chunked_results = list(pairwise_distances_chunked(
    706                 X, self._fit_X, reduce_func=reduce_func,
    707                 metric=self.effective_metric_, n_jobs=n_jobs,

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py in pairwise_distances_chunked(X, Y, reduce_func, metric, n_jobs, working_memory, **kwds)
   1621         else:
   1622             X_chunk = X[sl]
-> 1623         D_chunk = pairwise_distances(X_chunk, Y, metric=metric,
   1624                                      n_jobs=n_jobs, **kwds)
   1625         if ((X is Y or Y is None)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64
     65             # extra_args > 0
[9:29 PM]
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py in pairwise_distances(X, Y, metric, n_jobs, force_all_finite, **kwds)
   1788         func = partial(distance.cdist, metric=metric, **kwds)
   1789
-> 1790     return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
   1791
   1792

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py in _parallel_pairwise(X, Y, func, n_jobs, **kwds)
   1357
   1358     if effective_n_jobs(n_jobs) == 1:
-> 1359         return func(X, Y, **kwds)
   1360
   1361     # enforce a threading backend to prevent data communication overhead

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\pairwise.py in _pairwise_callable(X, Y, metric, force_all_finite, **kwds)
   1401         iterator = itertools.product(range(X.shape[0]), range(Y.shape[0]))
   1402         for i, j in iterator:
-> 1403             out[i, j] = metric(X[i], Y[j], **kwds)
   1404
   1405     return out

D:\Users\psalm\AppData\Local\Temp/ipykernel_41176/1088380155.py in mydist(x, y)
      3 # a distance metric function applying different weights for different features
      4 def mydist(x, y):
----> 5     return np.sum((x-y)**2 * np.array([0.75, 0.05, 0.03, 0.01, 0.01, 0.15, 0.05, 0.02]))
      6
      7 knn_regr = KNeighborsRegressor(n_neighbors=10, metric=mydist)

ValueError: operands could not be broadcast together with shapes (30,) (8,)