class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score=True)
1.estimator：
传入估计器与不需要调参的参数，每一个估计器都需要一个scoring参数。
2.param_grid：
需要最优化的参数的取值，值为字典或者列表。
3.scoring:
模型评价标准，默认None，这时需要使用score函数，根据所选模型不同，评价准则不同。字符串或者自定义形如：scorer(estimator, X, y)；如果是None，则使用estimator的误差估计函数。
4.n_jobs
n_jobs: 并行数，int：个数，-1：跟CPU核数一致。
5.refit=True
默认为True,程序将会以交叉验证训练集得到的最佳参数，重新对所有可用的训练集与开发集进行，作为最终用于性能评估的最佳模型参数。即在搜索参数结束后，用最佳参数结果再次fit一遍全部数据集。
6.pre_dispatch=‘2*n_jobs’
指定总共分发的并行任务数。当n_jobs大于1时，数据将在每个运行点进行复制，这可能导致OOM，而设置pre_dispatch参数，则可以预先划分总共的job数量，使数据最多被复制pre_dispatch次。
from sklearn.datasets import load_iris
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
X,y = load_iris(return_X_y=True)
df_X = pd.DataFrame(X,columns=list("ABCD"))
#gridSearchCV
parameters = [{'n_estimators':[10,100,1000],
'criterion':['entropy','gini'],
'max_depth':[10,50,100,200],
'min_samples_split':[2,5,10],
'min_weight_fraction_leaf':[0.0,0.1,0.2,0.3,0.4,0.5]}]
parameters = [{'n_estimators':[10,20]}]
#scoring="precision"或者"recall"或者"roc_auc","accuracy"或者None
clf = GridSearchCV(RandomForestClassifier(), parameters,cv=2,scoring="accuracy")
clf.fit(df_X,y)
clf.cv_results_
# =============================================================================
# {'mean_fit_time': array([0.0089916 , 0.01695275]),
# 'mean_score_time': array([0.00099409, 0.00148273]),
# 'mean_test_score': array([0.94666667, 0.96 ]),
# 'mean_train_score': array([0.98666667, 1. ]),
# 'param_n_estimators': masked_array(data=[10, 20],
# mask=[False, False],
# fill_value='?',
# dtype=object),
# 'params': [{'n_estimators': 10}, {'n_estimators': 20}],
# 'rank_test_score': array([2, 1]),
# 'split0_test_score': array([0.96, 0.96]),
# 'split0_train_score': array([1., 1.]),
# 'split1_test_score': array([0.93333333, 0.96 ]),
# 'split1_train_score': array([0.97333333, 1. ]),
# 'std_fit_time': array([1.01363659e-03, 9.53674316e-07]),
# 'std_score_time': array([4.17232513e-06, 5.05685806e-04]),
# 'std_test_score': array([0.01333333, 0. ]),
# 'std_train_score': array([0.01333333, 0. ])}
# =============================================================================
clf.best_estimator_
# =============================================================================
# RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
# max_depth=None, max_features='auto', max_leaf_nodes=None,
# min_impurity_decrease=0.0, min_impurity_split=None,
# min_samples_leaf=1, min_samples_split=2,
# min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=1,
# oob_score=False, random_state=None, verbose=0,
# warm_start=False)
# =============================================================================
clf.best_score_
# =============================================================================
# Out[42]: 0.96
#
# =============================================================================
clf.best_params_
# =============================================================================
# Out[43]: {'n_estimators': 20}
#
# =============================================================================
clf.grid_scores_
# =============================================================================
# [mean: 0.94667, std: 0.01333, params: {'n_estimators': 10},
# mean: 0.96000, std: 0.00000, params: {'n_estimators': 20}]
# =============================================================================
参考：http://blog.51cto.com/emily18/2088128
