我正在研究（我认为是什么）一个简单的二进制分类问题。我从参数网格搜索中得到了这个奇怪的结果，无论模型是什么参数，其f1-分数始终为〜0.75。我不确定这是否：a）反映了我对f1-分数误解为度量标准的误解，b）是由于数据或模型（我使用XGBoost）存在问题而需要更正的，或者c）只是表明模型参数基本上无关紧要，我得到的f1分数约为0.75。
更令人困惑的是，对于同一问题的两组完全不同的预测变量，我得到了相同的结果（例如，如果我正在预测房地产价值，一组正在使用邻里价格，而另一组正在使用房屋特征-不同同一问题的预测变量集）。一组参数的范围大约为0.67-0.82，具有近似正常的方差，而第二组参数（如下所示）的每个参数集的f1-得分几乎完全相同，为0.7477。
为了提供更多细节，当前数据集包含大约30,000个示例，一个类大约占示例的60％（另一个是40％）。我还没有深入研究这个新的数据集，但是对于以前的数据集，当我更仔细地检查一个模型时，我发现合理的精度和查全率值随不同的参数集而有所变化，这使我担心模型是只是猜测更普遍的一类。
我正在使用XGBoost，并使用scikit-learn的GridSearchCV。跳过导入等，网格搜索代码为
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，此处显示了一部分（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}
答案 0 :(得分：1)
让我们假设您的分类器将所有内容预测为多数类，那么您的：
precision = tp/(tp+fp) = 60/(60+40) = 0,6
recall = tp/(tp+fn) = 60/(60+0) = 1
和您的f1得分：
f1 = 2*precision*recall/(precision+recall)= 2*0,6*1/(0,6+1)
= 1,2/1,6= 0,75
因此，您的分类器probalby总是预测多数类别。
要一次检查您的confusion_matrix，可以使用以下命令：
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_true, y_pred))