【发布时间】：2019-11-14 02:50:52
【问题描述】：
我正在研究（我认为是）一个简单的二元分类问题。我从参数网格搜索中得到了这个奇怪的结果，无论参数是什么，模型总是返回 ~0.75 的 f1 分数。我不确定这是否：a) 反映了我对 f1 分数作为指标的误解，b) 是由于需要更正的数据或模型（我正在使用 XGBoost）存在问题，或者c) 只是表明模型参数基本上是不相关的，并且 f1 分数约为 0.75 与我得到的一样好。
更令人困惑的是，我对同一个问题的两组完全不同的预测变量得到了相同的结果（例如，如果我预测房地产价值，一组使用社区价格，另一组使用房屋特征——不同相同问题的预测变量集）。一组的范围约为 0.67-0.82，方差接近正常，而对于第二组（如下所示），每个参数集的 f1-score 几乎完全相同，均为 0.7477。
为了更详细一些，当前数据集有大约 30,000 个示例，一个类约占示例的 60%（另一个是 40%）。我还没有深入研究这个新数据集，但是对于之前的数据集，当我更仔细地检查一个模型时，我发现了合理的精度和召回值，随着不同的参数集发生了一些变化，这破坏了我对模型的担忧只是猜测更流行的类。
我正在使用 XGBoost，并使用 scikit-learn 的 GridSearchCV。跳过导入等网格搜索代码是
grid_values = {'n_estimators':[50,100,200,500,1000],'max_depth':[1,3,5,8], 'min_child_weight':range(1,6,2)}
clf=XGBClassifier()
grid_clf=GridSearchCV(clf,param_grid=grid_values,scoring='f1',verbose=10)
grid_clf.fit(game_records,hora)
print('Grid best score (f1): ', grid_clf.best_score_)
print('Grid best parameter (max. f1): ', grid_clf.best_params_)
https://pastebin.com/NSB0yaNi 的完整输出，此处显示部分（大部分）：
Fitting 3 folds for each of 60 candidates, totalling 180 fits
[CV] max_depth=1, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=1, min_child_weight=1, n_estimators=50, score=0.7477603583426652, total= 11.1s
[CV] max_depth=1, min_child_weight=1, n_estimators=50 ................
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 11.4s remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=50, score=0.74772504549909, total= 11.3s
[CV] max_depth=1, min_child_weight=1, n_estimators=50 ................
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 23.1s remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=50, score=0.7477773888694436, total= 11.2s
[CV] max_depth=1, min_child_weight=1, n_estimators=100 ...............
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 34.8s remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=100, score=0.7477603583426652, total= 21.4s
[CV] max_depth=1, min_child_weight=1, n_estimators=100 ...............
[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 56.8s remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=100, score=0.74772504549909, total= 21.3s
[CV] max_depth=1, min_child_weight=1, n_estimators=100 ...............
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.3min remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=100, score=0.7477773888694436, total= 21.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=200 ...............
[Parallel(n_jobs=1)]: Done 6 out of 6 | elapsed: 1.7min remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=200, score=0.7477603583426652, total= 41.3s
[CV] max_depth=1, min_child_weight=1, n_estimators=200 ...............
[Parallel(n_jobs=1)]: Done 7 out of 7 | elapsed: 2.4min remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=200, score=0.74772504549909, total= 41.1s
[CV] max_depth=1, min_child_weight=1, n_estimators=200 ...............
[Parallel(n_jobs=1)]: Done 8 out of 8 | elapsed: 3.1min remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=200, score=0.7477773888694436, total= 41.1s
[CV] max_depth=1, min_child_weight=1, n_estimators=500 ...............
[Parallel(n_jobs=1)]: Done 9 out of 9 | elapsed: 3.7min remaining: 0.0s
[CV] max_depth=1, min_child_weight=1, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=1, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=1, min_child_weight=1, n_estimators=500, score=0.74772504549909, total= 1.8min
[CV] max_depth=1, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=1, min_child_weight=1, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=1, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=1, min_child_weight=1, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=1, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=1, min_child_weight=1, n_estimators=1000, score=0.74772504549909, total= 3.4min
...
[CV] max_depth=3, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=1, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=3, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=100, score=0.7477603583426652, total= 21.2s
[CV] max_depth=3, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=100, score=0.74772504549909, total= 21.0s
[CV] max_depth=3, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=100, score=0.7477773888694436, total= 20.9s
[CV] max_depth=3, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=200, score=0.7477603583426652, total= 41.0s
[CV] max_depth=3, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=200, score=0.74772504549909, total= 41.2s
[CV] max_depth=3, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=200, score=0.7477773888694436, total= 41.4s
[CV] max_depth=3, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=3, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=3, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=1, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=3, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=1, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=3, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=1, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=3, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=1, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=3, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=3, n_estimators=50, score=0.7477603583426652, total= 10.9s
[CV] max_depth=3, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=3, n_estimators=50, score=0.74772504549909, total= 11.0s
[CV] max_depth=3, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=3, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=3, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=100, score=0.7477603583426652, total= 20.9s
[CV] max_depth=3, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=100, score=0.74772504549909, total= 21.0s
[CV] max_depth=3, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=100, score=0.7477773888694436, total= 21.0s
[CV] max_depth=3, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=200, score=0.7477603583426652, total= 41.2s
[CV] max_depth=3, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=200, score=0.74772504549909, total= 41.2s
[CV] max_depth=3, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=200, score=0.7477773888694436, total= 41.2s
[CV] max_depth=3, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=3, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=3, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=3, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=3, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=3, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=3, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=3, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=3, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=3, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=3, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=5, n_estimators=50, score=0.7477603583426652, total= 11.0s
[CV] max_depth=3, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=5, n_estimators=50, score=0.74772504549909, total= 10.9s
[CV] max_depth=3, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=3, min_child_weight=5, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=3, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=100, score=0.7477603583426652, total= 21.2s
[CV] max_depth=3, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=100, score=0.74772504549909, total= 21.0s
[CV] max_depth=3, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=100, score=0.7477773888694436, total= 21.0s
[CV] max_depth=3, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=200, score=0.7477603583426652, total= 41.1s
[CV] max_depth=3, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=200, score=0.74772504549909, total= 41.3s
[CV] max_depth=3, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=200, score=0.7477773888694436, total= 41.0s
[CV] max_depth=3, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=3, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=3, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=3, min_child_weight=5, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=3, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=5, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=3, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=5, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=3, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=3, min_child_weight=5, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=5, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=1, n_estimators=50, score=0.7477603583426652, total= 10.9s
[CV] max_depth=5, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=1, n_estimators=50, score=0.74772504549909, total= 10.9s
[CV] max_depth=5, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=1, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=5, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=100, score=0.7477603583426652, total= 21.0s
[CV] max_depth=5, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=100, score=0.74772504549909, total= 21.1s
[CV] max_depth=5, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=100, score=0.7477773888694436, total= 21.0s
[CV] max_depth=5, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=200, score=0.7477603583426652, total= 41.3s
[CV] max_depth=5, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=200, score=0.74772504549909, total= 41.1s
[CV] max_depth=5, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=200, score=0.7477773888694436, total= 41.1s
[CV] max_depth=5, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=5, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=5, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=1, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=5, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=1, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=5, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=1, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=5, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=1, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=5, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=3, n_estimators=50, score=0.7477603583426652, total= 10.9s
[CV] max_depth=5, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=3, n_estimators=50, score=0.74772504549909, total= 10.9s
[CV] max_depth=5, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=3, n_estimators=50, score=0.7477773888694436, total= 11.0s
[CV] max_depth=5, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=100, score=0.7477603583426652, total= 21.3s
[CV] max_depth=5, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=100, score=0.74772504549909, total= 20.9s
[CV] max_depth=5, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=100, score=0.7477773888694436, total= 20.9s
[CV] max_depth=5, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=200, score=0.7477603583426652, total= 41.1s
[CV] max_depth=5, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=200, score=0.74772504549909, total= 41.4s
[CV] max_depth=5, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=200, score=0.7477773888694436, total= 41.1s
[CV] max_depth=5, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=5, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=5, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=3, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=5, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=3, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=5, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=3, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=5, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=3, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=5, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=5, n_estimators=50, score=0.7477603583426652, total= 11.0s
[CV] max_depth=5, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=5, n_estimators=50, score=0.74772504549909, total= 11.0s
[CV] max_depth=5, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=5, min_child_weight=5, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=5, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=100, score=0.7477603583426652, total= 21.0s
[CV] max_depth=5, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=100, score=0.74772504549909, total= 21.0s
[CV] max_depth=5, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=100, score=0.7477773888694436, total= 21.8s
[CV] max_depth=5, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=200, score=0.7477603583426652, total= 41.2s
[CV] max_depth=5, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=200, score=0.74772504549909, total= 41.6s
[CV] max_depth=5, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=200, score=0.7477773888694436, total= 41.2s
[CV] max_depth=5, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=5, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=5, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=5, min_child_weight=5, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=5, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=5, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=5, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=5, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=5, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=5, min_child_weight=5, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=8, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=1, n_estimators=50, score=0.7477603583426652, total= 10.9s
[CV] max_depth=8, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=1, n_estimators=50, score=0.74772504549909, total= 10.9s
[CV] max_depth=8, min_child_weight=1, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=1, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=8, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=100, score=0.7477603583426652, total= 21.2s
[CV] max_depth=8, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=100, score=0.74772504549909, total= 21.0s
[CV] max_depth=8, min_child_weight=1, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=100, score=0.7477773888694436, total= 20.9s
[CV] max_depth=8, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=200, score=0.7477603583426652, total= 41.0s
[CV] max_depth=8, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=200, score=0.74772504549909, total= 41.4s
[CV] max_depth=8, min_child_weight=1, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=200, score=0.7477773888694436, total= 41.0s
[CV] max_depth=8, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=8, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=8, min_child_weight=1, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=1, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=8, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=1, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=8, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=1, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=8, min_child_weight=1, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=1, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=8, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=3, n_estimators=50, score=0.7477603583426652, total= 10.9s
[CV] max_depth=8, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=3, n_estimators=50, score=0.74772504549909, total= 10.9s
[CV] max_depth=8, min_child_weight=3, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=3, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=8, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=100, score=0.7477603583426652, total= 20.9s
[CV] max_depth=8, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=100, score=0.74772504549909, total= 21.0s
[CV] max_depth=8, min_child_weight=3, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=100, score=0.7477773888694436, total= 20.9s
[CV] max_depth=8, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=200, score=0.7477603583426652, total= 41.3s
[CV] max_depth=8, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=200, score=0.74772504549909, total= 41.1s
[CV] max_depth=8, min_child_weight=3, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=200, score=0.7477773888694436, total= 41.2s
[CV] max_depth=8, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=8, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=8, min_child_weight=3, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=3, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=8, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=3, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=8, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=3, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=8, min_child_weight=3, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=3, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[CV] max_depth=8, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=5, n_estimators=50, score=0.7477603583426652, total= 10.9s
[CV] max_depth=8, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=5, n_estimators=50, score=0.74772504549909, total= 10.9s
[CV] max_depth=8, min_child_weight=5, n_estimators=50 ................
[CV] max_depth=8, min_child_weight=5, n_estimators=50, score=0.7477773888694436, total= 10.9s
[CV] max_depth=8, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=100, score=0.7477603583426652, total= 20.9s
[CV] max_depth=8, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=100, score=0.74772504549909, total= 21.4s
[CV] max_depth=8, min_child_weight=5, n_estimators=100 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=100, score=0.7477773888694436, total= 21.0s
[CV] max_depth=8, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=200, score=0.7477603583426652, total= 41.2s
[CV] max_depth=8, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=200, score=0.74772504549909, total= 41.3s
[CV] max_depth=8, min_child_weight=5, n_estimators=200 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=200, score=0.7477773888694436, total= 41.0s
[CV] max_depth=8, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=500, score=0.7477603583426652, total= 1.7min
[CV] max_depth=8, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=500, score=0.74772504549909, total= 1.7min
[CV] max_depth=8, min_child_weight=5, n_estimators=500 ...............
[CV] max_depth=8, min_child_weight=5, n_estimators=500, score=0.7477773888694436, total= 1.7min
[CV] max_depth=8, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=5, n_estimators=1000, score=0.7477603583426652, total= 3.4min
[CV] max_depth=8, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=5, n_estimators=1000, score=0.74772504549909, total= 3.4min
[CV] max_depth=8, min_child_weight=5, n_estimators=1000 ..............
[CV] max_depth=8, min_child_weight=5, n_estimators=1000, score=0.7477773888694436, total= 3.4min
[Parallel(n_jobs=1)]: Done 180 out of 180 | elapsed: 227.8min finished
Grid best score (f1): 0.7477542636024276
Grid best parameter (max. f1): {'max_depth': 1, 'min_child_weight': 1, 'n_estimators': 50}
【问题讨论】：
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你的数据集中有多少样本？
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~30,000。另外，我只是想了一些更有用的信息来添加到主帖子中，现在就做
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混淆矩阵怎么样？你检查了吗？
标签： python machine-learning scikit-learn data-science xgboost