f1-score always ~0.75?

Question

I'm working on (what I think is) a straightforward binary classification problem. I'm getting this curious result from my parameter grid search that no matter what the parameters are the model always returns an f1-score of ~0.75. I'm unsure if this: a) reflects something I'm misunderstanding about the f1-score as a metric, b) is due to some problem with the data or model (I'm using XGBoost) that needs to be corrected, or c) just shows that the model parameters are basically irrelevant and an f1-score of ~0.75 is as good as I'll get.

Even more confusingly I got this same result for two entirely different sets of predictors for the same problem (eg if I was predicting real estate value, one set was using neighborhood prices, and the other set was using house characteristics -- different sets of predictors for the same problem). For one set the range was about 0.67-0.82 with an approximately normal variance, and for the second set (shown below) every parameter set gave nearly exactly the same f1-score of 0.7477.

To give some more detail, the current dataset has about 30,000 examples, one class is about 60% of the examples (the other is the 40%). I haven't delved deeply into this new dataset yet, but with the previous dataset, when I examined one model more closely, I found reasonable precision and recall values, which changed somewhat with different parameter sets, which ruined my concern that the model was just guessing the more prevalent class.

I'm using XGBoost, and using scikit-learn's GridSearchCV. Skipping imports etc the grid search code is

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_)

Full output at https://pastebin.com/NSB0yaNi, with a portion (most) shown here:

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}

f1-score always ~0.75?

Answers (1)

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