If I use F1 score as a metric, that classifier is going to get a low score. My classifier ignores the input and always returns the same prediction: “has flu.” The recall of this classifier is going to be 1 because I correctly classified all sick patients as sick, but the precision is near 0 because of a considerable number of false positives. Such a function is a perfect choice for the scoring metric of a classifier because useless classifiers get a meager score.įor example, if I created a fake “classifier” that tells a doctor whether a patient has the flu or not.
Print(stats.hmean()) # added one extremely small value Print(stats.hmean()) # added one small value Print(stats.hmean()) #added one extremely large value Print(stats.hmean()) #added one large value Extremely low values have a significant influence on the result. On the other hand, not all outliers are ignored. Because of that, the result is not sensitive to extremely large values. The harmonic mean is defined as the reciprocal of the arithmetic mean of the reciprocals. The F1 score is based on the harmonic mean. If one of the parameters is small, the second one no longer matters.Īs I mentioned at the beginning, F1 score emphasizes the lowest value. It looks that in this case precision is ignored, and the F1 score remain equal to 0. Let’s begin by looking at extreme values.įor example precision = 1 and recall = 0. Plot(all_values, all_values, all_values, f_score_label = 'F1 score') Sc = ax2.scatter(precision, recall, c = f_score, cmap=plt.cm.jet, vmin = 0, vmax = 1) 0.05s gpulist v4.Def plot(precision, recall, f_score, f_score_label):Īx = plt.subplot(gs, projection='3d')Īx.plot_trisurf(precision, recall, f_score, cmap=plt.cm.jet, linewidth=0.2, vmin = 0, vmax = 1)Īx.set_zlabel(f_score_label, rotation = 0) #10 got benchmark programm data, about to get FAL images +0s. #9 Created sortOrder for cached benchmark data +0s. #8 Loaded cached benchmark data tx_nbc2_gpugame_cache_values +0s. #7 found 5720 cached benchmark values +0.02s. #5 Cache table found with all needed fields in it! +0s. #4 Cache table for values found, checking fields. #2 cached gamecheck data found for bench uid 476 +0s. # started gpulist at took 0s on source +0s. Move your cursor over the value to see individual results. The value in the fields displays the average frame rate of all values in the database. Uncertain – This graphics card has not been explicitly tested on this game and no reliable interpolation can be made based on the performances of surrounding cards of the same class or family. A slower card may be able to achieve better and more consistent frame rates than this particular GPU running the same benchmark scene. Uncertain – This graphics card experienced unexpected performance issues during testing for this game. Based on interpolated information from surrounding graphics cards of similar performance levels, fluent frame rates are expected. May Run Fluently – This graphics card has not been explicitly tested on this game. Based on interpolated information from surrounding graphics cards of similar performance levels, stutters and poor frame rates are expected.įluent – Based on all known benchmarks using the specified graphical settings, this game should run at or above 25fpsįluent – Based on all known benchmarks using the specified graphical settings, this game should run at or above 35fpsįluent – Based on all known benchmarks using the specified graphical settings, this game should run at or above 58fps May Stutter – This graphics card has not been explicitly tested on this game. Based on all known benchmarks using the specified graphical settings, average frame rates are expected to fall below 25fps Stutters – This game is very likely to stutter and have poor frame rates. n123 Number of benchmarks for this median value / * Approximate position
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