为什么我会通过随机搜索估计器和具有最佳参数创建的新估计器获得不同的结果？

Question

随机搜索CV的估计量，我经历了意外的行为：

我正在寻找随机森林的最佳参数。当我确定所得最佳估计器的准确性时，与训练随机搜索最佳参数的新随机森林相比，我会获得不同的结果。这是为什么？

这是随机搜索的代码示例（只有很少的迭代）：

n_estimators = np.linspace(start=100,stop=2500,num=11, dtype=int)
max_features = ['sqrt',None , 0.2, 0.4]
max_depth = [10, 20, 50, 75, 100, 125, 150]
min_samples_split = [2, 5, 8, 11]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
criterion=['gini', 'entropy']
random_grid = {'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf,
               'bootstrap': bootstrap,
               'criterion': criterion}
rf_base=RandomForestClassifier()
rf_random=RandomizedSearchCV(estimator = rf_base, param_distributions = random_grid,
                             n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = -1)

training_features, training_labels = gd.get_data(1)
test_features, test_labels = gd.get_data(2)

rf_random.fit(training_features, training_labels)
print("The best estimator: ", rf_random.best_estimator_)
print("The best score: ", rf_random.best_score_)

print ('Training Accuracy: ', str(rf_random.score(training_features, training_labels)))
print ('Test Accuracy: ', str(rf_random.score(test_features,test_labels)))

返回exapmle的结果n_estimators = 1780，min_samples_split = 11，min_samples_leaf = 2， max_features = 0.2，max_depth = 20，criterion ='熵'，bootstrap ='false'和测试精度为0.8417，

但是当我使用这些参数训练新模型时，我会得到例如0,8339的测试准确性。代码看起来像这样：

training_features, training_labels = gd.get_data(1)
test_features, test_labels = gd.get_data(2)

rf=RandomForestClassifier()
rf.set_params(n_estimators=1780, min_samples_split=11, min_samples_leaf=2,
                            max_features=0.2, max_depth=20, criterion='entropy',bootstrap='False')

rf.fit(training_features, training_labels)
print('Training Accuracy: ', str(rf.score(training_features, training_labels)))
print ('Test Accuracy: ', str(rf.score(test_features,test_labels)))

Answer 1

解决方案是在两种情况下将随机_STATE设置为相同的值（新估算器缺少它）。