grid_search = GridSearchCV(knn_reg, param_grid, n_jobs=2, verbose=1)
grid_search.fit(X_train, y_train)

Fitting 3 folds for each of 60 candidates, totalling 180 fits

Traceback (most recent call last):

  File "<string>", line 1, in <module>

  File "D:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 105, in spawn_main

    exitcode = _main(fd)

  File "D:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 114, in _main

    prepare(preparation_data)

  File "D:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 225, in prepare

    _fixup_main_from_path(data['init_main_from_path'])

  File "D:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path

    run_name="__mp_main__")

  File "D:\ProgramData\Anaconda3\lib\runpy.py", line 263, in run_path

    pkg_name=pkg_name, script_name=fname)

  File "D:\ProgramData\Anaconda3\lib\runpy.py", line 96, in _run_module_code

    mod_name, mod_spec, pkg_name, script_name)

  File "D:\ProgramData\Anaconda3\lib\runpy.py", line 85, in _run_code

    exec(code, run_globals)

  File "E:\PythonProject\Regression.py", line 52, in <module>

    grid_search.fit(X_train, y_train)

  File "D:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 639, in fit

    cv.split(X, y, groups)))

  File "D:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 749, in __call__

    n_jobs = self._initialize_backend()

  File "D:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 547, in _initialize_backend

    **self._backend_args)

  File "D:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 305, in configure

    '[joblib] Attempting to do parallel computing '

ImportError: [joblib] Attempting to do parallel computing without protecting your import on a system that does not support forking. To use parallel-computing in a script, you must protect your main loop using "if __name__ == '__main__'". Please see the joblib documentation on Parallel for more information