关于RandomizedSearchCV 和GridSearchCV(区别：参数个数的选择方式)

窝窝插件

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 09 22:38:37 2016
@author: Administrator
"""
import time
import numpy as np
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.grid_search import RandomizedSearchCV
# 生成数据
digits = load_digits()
X, y = digits.data, digits.target
# 元分类器
meta_clf = RandomForestClassifier(n_estimators=20)
# =================================================================
# 设置参数
param_dist = {"max_depth": [3, None],
"max_features": [1,5,7,11],
"min_samples_split": [1,5,7,11],
"min_samples_leaf": [1,5,7,11],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
# 运行随机搜索 RandomizedSearch
n_iter_search = 20
rs_clf = RandomizedSearchCV(meta_clf, param_distributions=param_dist,n_iter=n_iter_search)
start = time.time()
rs_clf.fit(X, y)
print("RandomizedSearchCV took %.2f seconds for %d candidates parameter settings." % ((time.time() - start), n_iter_search))
print(rs_clf.grid_scores_)
# =================================================================
# 设置参数
param_grid = {"max_depth": [3, None],
"max_features": [1, 3, 10],
"min_samples_split": [1, 3, 10],
"min_samples_leaf": [1, 3, 10],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
# 运行网格搜索 GridSearch
gs_clf = GridSearchCV(meta_clf, param_grid=param_grid)
start = time.time()
gs_clf.fit(X, y)
print("GridSearchCV took %.2f seconds for %d candidate parameter settings." % (time.time() - start, len(gs_clf.grid_scores_)))
print(gs_clf.grid_scores_)
　　RandomizedSearchCV took 8.64 seconds for 20 candidates parameter settings.
[mean: 0.78075, std: 0.00987, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 11, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3},mean: 0.90874, std: 0.01377, params: {'bootstrap': False, 'min_samples_leaf': 7, 'max_features': 11, 'criterion': 'entropy', 'min_samples_split': 1, 'max_depth': None}, mean: 0.90929, std: 0.01682, params: {'bootstrap': True, 'min_samples_leaf': 5, 'max_features': 11, 'criterion': 'gini', 'min_samples_split': 1, 'max_depth': None}, mean: 0.91987, std: 0.01263, params: {'bootstrap': False, 'min_samples_leaf': 5, 'max_features': 5, 'criterion': 'gini', 'min_samples_split': 1, 'max_depth': None}, mean: 0.79911, std: 0.00789, params: {'bootstrap': False, 'min_samples_leaf': 11, 'max_features': 5, 'criterion': 'entropy', 'min_samples_split': 11, 'max_depth': 3}, mean: 0.91764, std: 0.01220, params: {'bootstrap': True, 'min_samples_leaf': 1, 'max_features': 11, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': None}, mean: 0.90874, std: 0.01730, params: {'bootstrap': False, 'min_samples_leaf': 11, 'max_features': 7, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': None}, mean: 0.76628, std: 0.01378, params: {'bootstrap': True, 'min_samples_leaf': 1, 'max_features': 1, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.79076, std: 0.01176, params: {'bootstrap': False, 'min_samples_leaf': 5, 'max_features': 7, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.91152, std: 0.01354, params: {'bootstrap': False, 'min_samples_leaf': 1, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': None}, mean: 0.78854, std: 0.02561, params: {'bootstrap': True, 'min_samples_leaf': 5, 'max_features': 5, 'criterion': 'entropy', 'min_samples_split': 1, 'max_depth': 3}, mean: 0.80078, std: 0.00885, params: {'bootstrap': False, 'min_samples_leaf': 1, 'max_features': 5, 'criterion': 'gini', 'min_samples_split': 5, 'max_depth': 3}, mean: 0.92098, std: 0.00853, params: {'bootstrap': False, 'min_samples_leaf': 5, 'max_features': 7, 'criterion': 'gini', 'min_samples_split': 5, 'max_depth': None}, mean: 0.83751, std: 0.01976, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 1, 'criterion': 'gini', 'min_samples_split': 5, 'max_depth': None}, mean: 0.82415, std: 0.01263, params: {'bootstrap': True, 'min_samples_leaf': 1, 'max_features': 5, 'criterion': 'entropy', 'min_samples_split': 11, 'max_depth': 3}, mean: 0.75348, std: 0.01295, params: {'bootstrap': True, 'min_samples_leaf': 5, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.83472, std: 0.00987, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 1, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': None}, mean: 0.80690, std: 0.01698, params: {'bootstrap': False, 'min_samples_leaf': 11, 'max_features': 5, 'criterion': 'gini', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.75014, std: 0.01576, params: {'bootstrap': True, 'min_samples_leaf': 11, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 7, 'max_depth': 3}, mean: 0.73901, std: 0.03013, params: {'bootstrap': True, 'min_samples_leaf': 7, 'max_features': 1, 'criterion': 'entropy', 'min_samples_split': 5, 'max_depth': 3}]
GridSearchCV took 83.70 seconds for 216 candidate parameter settings.
[mean: 0.74124, std: 0.04157, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.76405, std: 0.01263, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.75348, std: 0.03088, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.77852, std: 0.03227, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.75960, std: 0.03054, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.77017, std: 0.00473, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.74402, std: 0.01357, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.76183, std: 0.00884, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.77017, std: 0.03727, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': 3}, mean: 0.78186, std: 0.06008, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79633, std: 0.02073, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.80579, std: 0.02625, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79911, std: 0.02291, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.81135, std: 0.02082, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.81135, std: 0.01920, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.80301, std: 0.01797, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79243, std: 0.03765, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.80801, std: 0.01096, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': 3}, mean: 0.79911, std: 0.01477, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.80134, std: 0.00711, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.79744, std: 0.02293, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.79744, std: 0.03528, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.78019, std: 0.00396, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.80189, std: 0.00813, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.82359, std: 0.01467, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.82359, std: 0.01319, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.82805, std: 0.02549, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': 3}, mean: 0.89204, std: 0.00679, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.89872, std: 0.00912, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.89371, std: 0.01408, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.88759, std: 0.00447, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.87924, std: 0.01686, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.87535, std: 0.01052, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.84474, std: 0.01431, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.83306, std: 0.01119, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.83973, std: 0.00916, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 1, 'max_depth': None}, mean: 0.91486, std: 0.01694, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.92098, std: 0.00287, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.90929, std: 0.00997, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.91152, std: 0.00625, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.91152, std: 0.01342, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.90428, std: 0.00125, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.87590, std: 0.01876, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.88036, std: 0.00917, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.88815, std: 0.00252, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 3, 'max_depth': None}, mean: 0.92321, std: 0.00473, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.92321, std: 0.01109, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.91653, std: 0.01915, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.92321, std: 0.01501, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.92265, std: 0.02198, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.90317, std: 0.01417, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.88703, std: 0.01145, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.90150, std: 0.00629, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.89260, std: 0.01268, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'gini', 'max_features': 10, 'max_depth': None}, mean: 0.76628, std: 0.00791, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.77518, std: 0.00799, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.76238, std: 0.02122, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.76071, std: 0.02280, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.75403, std: 0.02968, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.73678, std: 0.00316, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.73011, std: 0.01752, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.73178, std: 0.03856, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.75292, std: 0.02576, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 1, 'max_depth': 3}, mean: 0.79967, std: 0.00800, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.80301, std: 0.01915, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.81859, std: 0.02460, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.79299, std: 0.01617, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.80634, std: 0.00903, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.79132, std: 0.01720, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.80022, std: 0.01453, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.80746, std: 0.02161, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.81191, std: 0.00745, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 3, 'max_depth': 3}, mean: 0.82415, std: 0.02950, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.78353, std: 0.00435, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.79744, std: 0.04214, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.81024, std: 0.02594, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.79132, std: 0.01355, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.81135, std: 0.01251, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.81302, std: 0.00774, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.80857, std: 0.00686, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.81135, std: 0.01074, params: {'bootstrap': True, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': 3}, mean: 0.90039, std: 0.00395, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.90150, std: 0.01320, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.87980, std: 0.01374, params: {'bootstrap': True, 'min_samples_leaf': 1, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 1, 'max_depth': None}, mean: 0.86255, std: 0.01479, params: {'bootstrap': True, 'min_samples_leaf': 3, 'min_samples_split': 1, 'criterion': 'entropy', 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'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92654, std: 0.01771, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.92599, std: 0.01215, params: {'bootstrap': False, 'min_samples_leaf': 3, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.90150, std: 0.02026, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 1, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.89649, std: 0.01794, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}, mean: 0.91208, std: 0.00546, params: {'bootstrap': False, 'min_samples_leaf': 10, 'min_samples_split': 10, 'criterion': 'entropy', 'max_features': 10, 'max_depth': None}]

        GridSearchCV:Does exhaustive search over a grid of parameters.RandomizedSearchCV:Randomized search on hyper parameters.　　RandomizedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
　　The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings.
　　In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.
　　If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.