ValueError: Invalid parameter

老规矩，先上源代码：

from sklearn.model_selection import GridSearchCV
param_grid =[{'n_eatimatiors':[3,10,30],'max_features':[2,4,6,8]},{'bootstrap':[False],'n_estimators':[3,10],'max_features':[2,3,4]},
]forest_reg=RandomForestRegressor(random_state=42)
grid_search=GridSearchCV(forest_reg,param_grid,cv=5,scoring='neg_mean_squared_error',return_train_score=True)
grid_search.fit(housing_prepared,housing_labels)

介绍下这段代码的应用方向和实现效果，
模型调参和网格搜索
1.手动调整超参数，找到很好的组合时很困难的
2.使用GridSearchCV替你进行搜索，告诉GridSearchCV，进行试验的超参数是什么，以及需要尝试的值，他会使用交叉验证评估所有超参数的可能组合

上述代码运行后的效果：

ValueError                                Traceback (most recent call last)
<ipython-input-353-54613827b5ad> in <module>8 grid_search=GridSearchCV(forest_reg,param_grid,cv=5,9                         scoring='neg_mean_squared_error',return_train_score=True)
---> 10 grid_search.fit(housing_prepared,housing_labels)d:\python3.8.5\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)70                           FutureWarning)71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)73     return inner_f74 d:\python3.8.5\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)734                 return results735
--> 736             self._run_search(evaluate_candidates)737 738         # For multi-metric evaluation, store the best_index_, best_params_ andd:\python3.8.5\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)1186     def _run_search(self, evaluate_candidates):1187         """Search all candidates in param_grid"""
-> 1188         evaluate_candidates(ParameterGrid(self.param_grid))1189 1190 d:\python3.8.5\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)706                               n_splits, n_candidates, n_candidates * n_splits))707
--> 708                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),709                                                        X, y,710                                                        train=train, test=test,d:\python3.8.5\lib\site-packages\joblib\parallel.py in __call__(self, iterable)1046             # remaining jobs.1047             self._iterating = False
-> 1048             if self.dispatch_one_batch(iterator):1049                 self._iterating = self._original_iterator is not None1050 d:\python3.8.5\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)864                 return False865             else:
--> 866                 self._dispatch(tasks)867                 return True868 d:\python3.8.5\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)782         with self._lock:783             job_idx = len(self._jobs)
--> 784             job = self._backend.apply_async(batch, callback=cb)785             # A job can complete so quickly than its callback is786             # called before we get here, causing self._jobs tod:\python3.8.5\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)206     def apply_async(self, func, callback=None):207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)209         if callback:210             callback(result)d:\python3.8.5\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)570         # Don't delay the application, to avoid keeping the input571         # arguments in memory
--> 572         self.results = batch()573 574     def get(self):d:\python3.8.5\lib\site-packages\joblib\parallel.py in __call__(self)260         # change the default number of processes to -1261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)263                     for func, args, kwargs in self.items]264 d:\python3.8.5\lib\site-packages\joblib\parallel.py in <listcomp>(.0)260         # change the default number of processes to -1261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262             return [func(*args, **kwargs)263                     for func, args, kwargs in self.items]264 d:\python3.8.5\lib\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)518             cloned_parameters[k] = clone(v, safe=False)519
--> 520         estimator = estimator.set_params(**cloned_parameters)521 522     start_time = time.time()d:\python3.8.5\lib\site-packages\sklearn\base.py in set_params(self, **params)247             key, delim, sub_key = key.partition('__')248             if key not in valid_params:
--> 249                 raise ValueError('Invalid parameter %s for estimator %s. '250                                  'Check the list of available parameters '251                                  'with `estimator.get_params().keys()`.' %ValueError: Invalid parameter n_eatimatiors for estimator RandomForestRegressor(max_features=2, random_state=42). Check the list of available parameters with `estimator.get_params().keys()`

错误的提示很长，核心是看最后几行，看箭头指向的249和最终的错误提示，

 247             key, delim, sub_key = key.partition('__')248             if key not in valid_params:
--> 249                 raise ValueError('Invalid parameter %s for estimator %s. '250                                  'Check the list of available parameters '251                                  'with `estimator.get_params().keys()`.' %ValueError: Invalid parameter n_eatimatiors for estimator RandomForestRegressor(max_features=2, random_state=42). Check the list of available parameters with `estimator.get_params().keys()`.

仔细分析这段代码，先定位"n_eatimatiors “,
原因是"Invalid parameter”,
结论是:“n_eatimatiors"是"Invalid parameter”,
于是接下来快速找到"n_eatimatiors",先检查书写的正确性，

param_grid =[{'n_eatimatiors':[3,10,30],'max_features':[2,4,6,8]},{'bootstrap':[False],'n_estimators':[3,10],'max_features':[2,3,4]},
]

确实是书写有问题，“n_eatimatiors"更新为"n_estimators”，

param_grid =[{'n_estimators':[3,10,30],'max_features':[2,4,6,8]},{'bootstrap':[False],'n_estimators':[3,10],'max_features':[2,3,4]},
]

重新运行OK。

GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),param_grid=[{'max_features': [2, 4, 6, 8],'n_estimators': [3, 10, 30]},{'bootstrap': [False], 'max_features': [2, 3, 4],'n_estimators': [3, 10]}],return_train_score=True, scoring='neg_mean_squared_error')

心得体会：写代码的过程中会经常遇到各种各样的问题，学会分析和解决问题至关重要，多去训练这方面的能力。

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