Machine learning scikit learn GridSearchCV无法正确处理随机林

我有一个用于随机森林模型的网格搜索实现

train_X, test_X, train_y, test_y = train_test_split(features, target, test_size=.10, random_state=0)
# A bit performance gains can be obtained from standarization
train_X, test_X = standarize(train_X, test_X)

tuned_parameters = [{
    'n_estimators': [5],
    'criterion': ['mse', 'mae'],
    'random_state': [0]
}]

scores = ['neg_mean_squared_error', 'neg_mean_absolute_error']
for n_fold in [5]:
    for score in scores:
        print("# Tuning hyper-parameters for %s with %d-fold" % (score, n_fold))
        start_time = time.time()
        print()

        # TODO: RandomForestRegressor
        clf = GridSearchCV(RandomForestRegressor(verbose=2), tuned_parameters, cv=n_fold,
                           scoring=score, verbose=2, n_jobs=-1)
        clf.fit(train_X, train_y)
        ... Rest omitted

在我使用它进行网格搜索之前，我已经在许多其他任务中使用了完全相同的数据集，因此数据应该没有任何问题。此外，出于测试目的，我首先使用线性回归来查看整个管道是否顺利运行。然后我切换到RandomForestRegressionor，并设置一个非常小的估计量来再次测试它。一件非常奇怪的事情发生在他们身上，我会附上详细的信息。性能有很大的下降，我不知道发生了什么。没有理由花费30分钟以上的时间来运行一个小型网格搜索

Fitting 5 folds for each of 2 candidates, totalling 10 fits
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.1s remaining:    0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.1s remaining:    0.0s
building tree 2 of 5
building tree 3 of 5
building tree 3 of 5
building tree 3 of 5
building tree 3 of 5
building tree 4 of 5
building tree 4 of 5
building tree 4 of 5
building tree 4 of 5
building tree 5 of 5
building tree 5 of 5
building tree 5 of 5
building tree 5 of 5
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    5.0s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    5.0s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    5.0s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    5.0s finished
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.3s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.3s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.8s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total=   5.3s
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.8s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total=   5.3s
building tree 1 of 5
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.9s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total=   5.3s
building tree 1 of 5
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.9s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total=   5.3s
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s
building tree 2 of 5
building tree 3 of 5
building tree 4 of 5
building tree 5 of 5
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    5.3s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.2s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total=   5.6s
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
building tree 1 of 5

上面的日志是在几秒钟内打印出来的，然后事情似乎从这里开始受阻

[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  7.4min remaining:    0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  7.5min remaining:    0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  7.5min remaining:    0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  7.8min remaining:    0.0s
building tree 2 of 5
building tree 3 of 5
building tree 3 of 5
building tree 3 of 5
building tree 3 of 5
building tree 4 of 5
building tree 4 of 5
building tree 4 of 5
building tree 4 of 5
building tree 5 of 5
building tree 5 of 5
building tree 5 of 5

这些线路需要20多分钟

顺便说一句，对于每次GridSearchCV运行，线性回归的成本不到1秒

你知道为什么性能下降那么多吗

---

如有任何建议和意见，我们将不胜感激。谢谢。

尝试为RandomForestRegressionor设置

最大深度。这样可以缩短装配时间。默认情况下
max\u depth=None

例如：

tuned_parameters = [{
    'n_estimators': [5],
    'criterion': ['mse', 'mae'],
    'random_state': [0],
    'max_depth': [4],
}]

编辑：默认情况下，
随机森林回归器
具有
n_jobs=1
。它将使用此设置一次构建一棵树。尝试设置
n\u作业=-1

此外，您可以指定多个指标，而不是将
评分
参数循环到
GridSearchCV
。执行此操作时，还必须指定要选择的度量值
GridSearchCV
作为
refit
的值。然后，您可以在拟合后访问
cv\u结果\u
字典中的所有分数

    clf = GridSearchCV(RandomForestRegressor(verbose=2),tuned_parameters, 
                       cv=n_fold, scoring=scores, refit='neg_mean_squared_error',
                       verbose=2, n_jobs=-1)

    clf.fit(train_X, train_y)
    results = clf.cv_results_
    print(np.mean(results['mean_test_neg_mean_squared_error']))
    print(np.mean(results['mean_test_neg_mean_absolute_error']))

相反，在
n_jobs=-1
上，请先尝试
n_jobs=1
。@VivekKumar这是我的第一次尝试，实际上没有成功……我会尝试一下，但为什么前几棵树建得那么快，而后几棵树却被拧死了。。。