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_f
74
d:\python3.8.5\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
734 return results
735
--> 736 self._run_search(evaluate_candidates)
737
738 # For multi-metric evaluation, store the best_index_, best_params_ and
d:\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 None
1050
d:\python3.8.5\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
864 return False
865 else:
--> 866 self._dispatch(tasks)
867 return True
868
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 is
786 # called before we get here, causing self._jobs to
d:\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 input
571 # 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 -1
261 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 -1
261 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。