Leaderboard

Fallstudie in Supervised Machine Learning

Profit-Verlauf

Abgabe 1

Gruppe	TP	TN	FP	FN	Sensitivität	Spezifität	Accuracy	Precision	Lift	Profit ↓	AUROC
Component Principles	488	99,239	172	101	0.828523	0.998270	0.997270	0.739394	125.533776	0.17320	0.990323
Overfit Avengers	479	99,258	153	110	0.813243	0.998461	0.997370	0.757911	128.677656	0.16310	0.989966
Bias Busters	469	99,191	220	120	0.796265	0.997787	0.996600	0.680697	115.568194	0.12985	0.980516
ROC Stars	458	99,255	156	131	0.777589	0.998431	0.997130	0.745928	126.643181	0.12770	0.981501
Overfit Avengers 2	438	99,327	84	151	0.743633	0.999155	0.997650	0.839080	142.458482	0.11270	0.977388
Feature Engineers	419	99,185	226	170	0.711375	0.997727	0.996040	0.649612	110.290731	0.04585	0.967008
Random Forest Rangers	428	98,726	685	161	0.726655	0.993109	0.991540	0.384546	65.287992	-0.05405	0.976722

Abgabe 2

Gruppe	TP	TN	FP	FN	Sensitivität	Spezifität	Accuracy	Precision	Lift	Profit ↓	AUROC
Overfit Avengers	505	99,247	164	84	0.857385	0.998350	0.997520	0.754858	128.159252	0.20325	0.994506
ROC Stars	466	99,265	146	123	0.791171	0.998531	0.997310	0.761438	129.276385	0.14340	0.989301
Overfit Avengers 2	485	99,137	274	104	0.823430	0.997244	0.996220	0.638999	108.488741	0.14275	0.991593
Bias Busters	484	99,128	283	105	0.821732	0.997153	0.996120	0.631030	107.135821	0.13885	0.988526
Component Principles	458	99,254	157	131	0.777589	0.998421	0.997120	0.744715	126.437258	0.12745	0.992070
Random Forest Rangers	457	99,172	239	132	0.775891	0.997596	0.996290	0.656609	111.478641	0.10530	0.989205
Feature Engineers	424	99,367	44	165	0.719864	0.999557	0.997910	0.905983	153.817132	0.09960	0.989357

Abgabe 3

Gruppe	TP	TN	FP	FN	Sensitivität	Spezifität	Accuracy	Precision	Lift	Profit ↓	AUROC
Overfit Avengers	466	99,321	137	76	0.859779	0.998623	0.997870	0.772803	142.583515	0.19265	0.994531
Feature Engineers	463	99,231	227	79	0.854244	0.997718	0.996940	0.671014	123.803412	0.16520	0.992644
Component Principles	454	99,284	174	88	0.837638	0.998251	0.997380	0.722930	133.381907	0.16360	0.994376
Overfit Avengers 2	441	99,302	156	101	0.813653	0.998431	0.997430	0.738693	136.290308	0.14665	0.990297
Bias Busters	448	99,247	211	94	0.826568	0.997879	0.996950	0.679818	125.427658	0.14445	0.992105
ROC Stars	463	99,123	335	79	0.854244	0.996632	0.995860	0.580201	107.048063	0.13820	0.992803
Random Forest Rangers	259	99,409	49	283	0.477860	0.999507	0.996680	0.840909	155.149279	-0.12690	0.964472

Abgabe 4

Gruppe	TP	TN	FP	FN	Sensitivität	Spezifität	Accuracy	Precision	Lift	Profit ↓	AUROC
Overfit Avengers	515	99,262	151	72	0.877342	0.998481	0.997770	0.773273	131.733096	0.22500	0.993958
Feature Engineers	502	99,263	150	85	0.855196	0.998491	0.997650	0.769939	131.165017	0.20380	0.993776
Random Forest Rangers	503	99,216	197	84	0.856899	0.998018	0.997190	0.718571	122.414213	0.19370	0.992796
Component Principles	496	99,225	188	91	0.844974	0.998109	0.997210	0.725146	123.534276	0.18440	0.993706
Overfit Avengers 2	481	99,298	115	106	0.819421	0.998843	0.997790	0.807047	137.486709	0.17790	0.992410
Bias Busters	477	99,318	95	110	0.812606	0.999044	0.997950	0.833916	142.064069	0.17630	0.991890
ROC Stars	461	99,330	83	126	0.785349	0.999165	0.997910	0.847426	144.365668	0.15290	0.993277