Background:

A bank wishes to develop a machine learning algorithm to improve its loan approval process. The bank has tracked the characteristicsdemographic and behavioralof 1,000 of its past customers, The bank would like to develop a classification model to represent the credit risks (good versus bad) of loan applicants. This model can then be applied to predict the credit risk of loan applicants in the future.

Predicting Credit Risk Problem

The bank has trained 11 different machine learning algorithms on this data set so that the bank can use loan applicant characteristics to predict whether a loan applicant with a given set of characteristics will be a good credit risk or not.

For this purpose, the analytics manager, Joanne, at the bank partitioned the 1,000 past customers into two groups: a training sample (800 past customers) and a validation sample (200 past customers in the table below). She used Actual Class as the output variable and all other variables input variables, and then she built 11 supervised machine learning algorithms using the data from the training sample.

Joanne then tested the efficacy of these 11 trained algorithms on the validation sample.

In the data table, you are given the results of Joanne's validation tests using these 11 algorithms on the validation sample. Each row in this table corresponds to a different customer. In column A (Actual Class), it is reported whether the customer is a good or bad credit risk (i.e., 0 = bad credit risk, 1 = good credit risk). In the remaining columns B to L, Joanne reports the predictions yielded by the 11 machine learning algorithms.

For example, the logistic regression algorithm (column C) predicts that there is a 57.66 percent chance that customer 1 is a good credit risk, while the boosted trees algorithm (column F) predicts that there is a 66.03 percent chance that customer 1 is a good risk. As another example, the logistic regression algorithm predicts a 12.95 percent chance that customer 3 is a good credit risk, while the boosted trees algorithm predicts a 38.12 percent chance.

	A	B	C	D	E	F	G	H	I	J	K	L
Customer	Actual Class	p(DA)	p(LR)	p(kNN)	p(CT-Single)	p(CT-Boosting)	p(CT-Bagging)	p(CT-Random)	p(NB)	p(NN-Boosting)	p(NN-Bagging)	p(NN-Manual)
1	1	0.54644211	0.576677264	1	0.709677419	0.66032089	0.66	0.78	0.279979928	0.500227536	0.8	0.295912775
2	1	0.647476351	0.636636942	1	0.853932584	0.643805743	1	0.9	0.787618812	0.746186009	0.72	0.677766144
3	0	0.117100891	0.129587792	0	0.37654321	0.381253309	0.1	0.4	0.081929129	0.30456647	0.12	0.096338169
4	1	0.91677927	0.901788998	1	0.853932584	0.514601361	0.96	0.96	0.710312836	0.739293776	1	0.529861638
5	1	0.941682966	0.938652125	1	0.853932584	0.601394894	1	0.98	0.936283767	0.83633934	1	0.983521365
6	1	0.88557433	0.88021933	1	0.853932584	0.601383151	0.94	0.86	0.933619205	0.783965275	0.9	0.962927153
7	0	0.121465689	0.122398517	1	0.37654321	0.414555506	0.18	0.46	0.189932482	0.264870189	0.02	0.086591973
8	1	0.630053056	0.59662642	0	0.853932584	0.418532162	0.88	0.54	0.884278633	0.477433714	0.44	0.841567078
9	1	0.423095124	0.439112471	1	0.37654321	0.460833918	0.3	0.64	0.351875729	0.292328652	0.32	0.650293109
10	1	0.832536544	0.817273972	1	0.853932584	0.507728605	0.98	1	0.901253074	0.654063168	0.78	0.545134569
11	1	0.938165671	0.93388581	1	0.715517241	0.624934391	0.94	1	0.939490493	0.823024346	1	0.980545302
12	1	0.951805117	0.949305466	1	0.853932584	0.634280012	0.98	0.96	0.962795389	0.897148609	1	0.966365884
13	1	0.873148241	0.849180823	1	0.853932584	0.521988104	0.98	0.98	0.917775019	0.701139995	0.92	0.913150252
14	0	0.443681272	0.482513911	1	0.37654321	0.495475095	0.28	0.44	0.100687365	0.417207477	0.2	0.124997251
15	0	0.235256475	0.209603036	0	0.37654321	0.49676042	0.24	0.44	0.140326569	0.375614775	0.32	0.129418266
16	0	0.58984257	0.580216535	0	0.715517241	0.575711164	0.52	0.68	0.342941602	0.380826244	0.4	0.764070239
17	1	0.748243	0.742009926	1	0.853932584	0.52013601	0.98	0.96	0.881074162	0.717110382	0.7	0.716466763
18	1	0.904962159	0.89404812	1	0.853932584	0.652524123	1	0.98	0.92398185	0.696146408	0.98	0.937351528
19	1	0.928284197	0.9566304	1	0.715517241	0.689151837	1	0.96	0.959348004	0.919816639	1	0.997943049
20	1	0.965456181	0.96460305	1	0.853932584	0.762106653	1	1	0.965235123	0.875644578	1	0.991872003
21	1	0.987114245	0.986937253	1	0.853932584	0.697607323	0.98	1	0.99357902	0.962265875	1	0.999764084
22	0	0.817096446	0.815654585	0	0.853932584	0.599433865	0.98	0.84	0.870804764	0.567444539	0.84	0.361120755
23	0	0.7713926	0.751585194	0	0.715517241	0.612824278	0.66	0.76	0.696381877	0.800345303	0.86	0.942326638
24	1	0.970731684	0.97353606	1	0.853932584	0.728419993	1	0.96	0.984641382	0.840820387	1	0.997168171
25	0	0.212559623	0.221232594	1	0.263157895	0.396216763	0.32	0.34	0.192546814	0.26080019	0.06	0.054127074
26	1	0.978720602	0.985085614	1	0.853932584	0.646640121	1	1	0.994941676	0.856339084	1	0.990427062
27	1	0.924619337	0.91478748	0	0.853932584	0.676710051	0.96	0.92	0.880298683	0.761355542	0.96	0.700786384
28	0	0.436739898	0.479216891	1	0.715517241	0.617937511	0.78	0.8	0.512514459	0.539398742	0.44	0.714481999
29	1	0.58041862	0.576999489	1	0.715517241	0.631802131	0.74	0.88	0.304513323	0.38094022	0.46	0.095347941
30	1	0.944603867	0.948046174	1	0.853932584	0.604235792	0.98	0.94	0.927663289	0.816917331	1	0.976487067
31	1	0.990253663	0.989921974	1	0.853932584	0.746829274	1	1	0.990027443	0.958941528	1	0.999710023
32	1	0.856938079	0.846461964	1	0.853932584	0.660227184	1	0.98	0.874039274	0.695596096	0.74	0.94309814
33	0	0.765939003	0.744554462	1	0.37654321	0.473377971	0.44	0.76	0.405226596	0.669400218	0.9	0.518204668
34	1	0.6946397	0.704524522	0	0.853932584	0.473858291	0.62	0.62	0.862902005	0.690680841	0.84	0.938759366
35	1	0.815397033	0.827949128	1	0.709677419	0.617437845	0.7	0.74	0.788909736	0.746011099	0.76	0.842911792
36	1	0.946326743	0.947978108	1	0.853932584	0.517552471	1	0.96	0.769243764	0.88202398	1	0.990971989
37	1	0.11055805	0.139719528	0	0.37654321	0.510601517	0.4	0.32	0.169611682	0.40275359	0.16	0.106390635
38	1	0.97181399	0.963015494	1	0.853932584	0.524116892	0.78	0.94	0.921088712	0.661087509	1	0.640780161
39	1	0.984365041	0.988520514	1	0.853932584	0.637108013	0.86	0.98	0.974339622	0.874151121	0.98	0.878749124
40	1	0.943221927	0.942148198	1	0.853932584	0.60123843	1	0.94	0.921454835	0.761879429	0.94	0.901972577
41	1	0.96625105	0.968960826	1	0.853932584	0.616734614	0.98	0.96	0.957388053	0.960049824	1	0.970999548
42	1	0.969170259	0.970085127	1	0.853932584	0.666187434	1	1	0.996674149	0.957001742	1	0.999824775
43	0	0.675955158	0.662336925	1	0.853932584	0.579730207	0.84	0.72	0.782090723	0.612279054	0.86	0.924624781
44	0	0.312259735	0.376148757	0	0.37654321	0.457197851	0.14	0.36	0.381715703	0.286921699	0.08	0.270021211
45	1	0.878401869	0.867966336	0	0.853932584	0.4262023	0.84	0.86	0.741499684	0.577942678	0.78	0.556180921
46	0	0.106627842	0.119701675	0	0.37654321	0.452881276	0.12	0.32	0.106464901	0.285745225	0	0.321012569
47	1	0.700841252	0.667916721	1	0.715517241	0.564619835	0.76	0.9	0.667842392	0.542730331	0.84	0.85968054
48	1	0.951048392	0.947582794	1	0.853932584	0.67125989	0.98	1	0.991012092	0.781876985	0.98	0.988058929
49	1	0.828973724	0.795623389	0	0.853932584	0.596889045	1	0.98	0.726708121	0.745241962	0.94	0.961784887
50	1	0.985208337	0.986652121	1	0.853932584	0.742764709	0.98	0.96	0.975975964	0.855365859	1	0.994808016
51	1	0.903250633	0.897856983	1	0.853932584	0.669279346	0.84	0.88	0.537096935	0.764244025	0.78	0.989442321
52	1	0.787970685	0.794870761	1	0.715517241	0.580180885	0.92	0.9	0.947074711	0.666649468	0.84	0.826142846
53	1	0.521972115	0.491825624	1	0.715517241	0.530028704	0.7	0.66	0.71577088	0.438898897	0.28	0.035863326
54	1	0.867472663	0.871237596	1	0.853932584	0.455201122	0.88	0.9	0.872865304	0.798590318	0.94	0.967988331
55	1	0.984846719	0.986490298	0	0.853932584	0.624186781	1	0.96	0.991405966	0.939946909	1	0.999353506
56	1	0.964774446	0.961401521	1	0.853932584	0.733644472	1	1	0.989418902	0.897357413	1	0.995010317
57	1	0.956054999	0.973928813	1	0.853932584	0.716008879	1	0.94	0.981704394	0.859885215	1	0.999618998
58	1	0.983451855	0.981422349	0	0.853932584	0.672863491	0.98	1	0.994238223	0.937810767	1	0.99656726
59	0	0.479703182	0.453879623	1	0.715517241	0.578834017	0.8	0.88	0.335229024	0.482468853	0.46	0.419125346
60	1	0.939049266	0.945122708	1	0.853932584	0.59282116	0.94	0.72	0.97891774	0.899526415	1	0.992782437
61	0	0.959626012	0.97226327	0	0.715517241	0.673843408	0.4	0.88	0.686914103	0.883611473	1	0.956083625
62	0	0.408556569	0.502713181	1	0.709677419	0.639071358	0.52	0.62	0.387584643	0.614980248	0.5	0.813981289
63	0	0.247042873	0.266422275	1	0.37654321	0.356713425	0.32	0.44	0.413779993	0.459409095	0.34	0.285626254
64	1	0.775544182	0.749762504	1	0.715517241	0.614929625	0.72	0.9	0.634353337	0.580573986	0.88	0.955041284
65	1	0.900321142	0.912875948	1	0.853932584	0.583985479	0.94	0.7	0.798705161	0.823999811	0.96	0.980723911
66	1	0.916756823	0.924987095	1	0.37654321	0.68061451	0.58	0.8	0.709210591	0.874444408	0.96	0.901633962
67	0	0.573821539	0.573714281	0	0.37654321	0.52544779	0.44	0.66	0.519857418	0.42156987	0.42	0.745703136
68	1	0.776488392	0.763082097	1	0.715517241	0.588289647	0.48	0.84	0.752911572	0.494583455	0.84	0.928273364
69	1	0.732829678	0.700574688	1	0.853932584	0.557900953	0.98	0.78	0.740772752	0.734295237	0.82	0.881012313
70	1	0.918428243	0.92206372	0	0.853932584	0.498949733	0.72	0.92	0.981486403	0.603208026	0.84	0.9321958
71	1	0.87368293	0.858553132	1	0.853932584	0.62077136	0.98	0.92	0.851521566	0.669356446	0.86	0.951631485
72	1	0.876138461	0.860349783	0	0.715517241	0.692644318	0.98	0.92	0.8977135	0.778812386	0.88	0.903499137
73	1	0.537161074	0.547849646	1	0.853932584	0.5312174	0.84	0.72	0.64524604	0.477066154	0.4	0.212739183
74	1	0.862472606	0.827180779	1	0.715517241	0.571131051	0.7	0.88	0.933892627	0.765691782	0.98	0.691410325
75	0	0.20464521	0.196073884	0	0.715517241	0.418759851	0.34	0.56	0.094850449	0.219532208	0.14	0.037866485
76	0	0.95678755	0.945378728	0	0.853932584	0.521065705	1	0.96	0.972002488	0.691095847	0.98	0.876184456
77	1	0.964959309	0.963961129	0	0.715517241	0.517367523	0.62	0.92	0.98106078	0.901935529	0.96	0.988089604
78	1	0.93480622	0.919519497	0	0.853932584	0.65130153	1	0.98	0.946458047	0.647248503	1	0.968385822
79	1	0.350339264	0.361101807	1	0.37654321	0.43188361	0.36	0.52	0.172463989	0.482519848	0.3	0.146822453
80	1	0.351205027	0.396746789	1	0.709677419	0.520206817	0.4	0.3	0.38172674	0.476421573	0.32	0.017008454
81	1	0.924729463	0.9196268	1	0.853932584	0.641623277	0.98	0.94	0.920274415	0.760488907	1	0.942414486
82	1	0.911337526	0.904088406	1	0.853932584	0.649382352	0.9	0.96	0.901162038	0.800567076	1	0.855118998
83	0	0.809083646	0.846212083	1	0.853932584	0.60790796	0.88	0.9	0.68393418	0.699728537	0.96	0.54314937
84	0	0.238409973	0.303234855	0	0.709677419	0.544459385	0.46	0.32	0.665513185	0.341096767	0.24	0.175154278
85	1	0.518405713	0.54407636	1	0.37654321	0.448217309	0.26	0.5	0.841108745	0.76489749	0.5	0.104313883
86	1	0.602578756	0.595488508	1	0.853932584	0.482837774	0.82	0.58	0.344349051	0.716179302	0.78	0.972331215
87	1	0.787539516	0.797048955	1	0.853932584	0.54228526	0.98	0.84	0.732170543	0.476030355	0.62	0.170929471
88	1	0.991778366	0.994864603	0	0.853932584	0.746626821	1	1	0.995807414	0.940977611	1	0.999986295
89	0	0.578863326	0.601366184	1	0.715517241	0.629492403	0.86	0.78	0.326657839	0.625687458	0.54	0.900984639
90	0	0.246470395	0.271120368	1	0.37654321	0.573932812	0.3	0.54	0.199127153	0.480286401	0.46	0.460086544
91	1	0.423407895	0.474695053	1	0.37654321	0.488161099	0.24	0.34	0.067438531	0.559629002	0.54	0.899260581
92	1	0.96674429	0.960252498	1	0.853932584	0.569998767	0.98	1	0.90879853	0.825009361	1	0.9978494
93	1	0.770399758	0.731257579	1	0.853932584	0.597836579	1	0.96	0.959624122	0.697756958	0.84	0.431096231
94	0	0.393168169	0.382743023	1	0.715517241	0.562132858	0.96	0.8	0.487712869	0.477693518	0.3	0.107933974
95	0	0.288920013	0.308928414	0	0.37654321	0.388074847	0.08	0.36	0.082659951	0.340312911	0.14	0.060593542
96	1	0.933090586	0.938576287	1	0.853932584	0.619494527	1	1	0.993412606	0.899277384	0.96	0.958110502
97	0	0.128826294	0.139603956	1	0.37654321	0.391580181	0.2	0.48	0.114273275	0.214428003	0.22	0.249343621
98	0	0.124447984	0.121158278	1	0.37654321	0.3241525	0.06	0.38	0.056221139	0.220811737	0.02	0.023692895
99	0	0.573266555	0.567584409	1	0.37654321	0.440456758	0.6	0.8	0.348356091	0.49527656	0.52	0.422763328
100	1	0.45212452	0.410222217	1	0.715517241	0.414568481	0.32	0.68	0.421069483	0.57776775	0.48	0.272211176
101	0	0.150675919	0.156972591	1	0.263157895	0.464231679	0.28	0.56	0.220431392	0.440381172	0.2	0.090822681
102	0	0.612452822	0.612478412	1	0.853932584	0.605241887	0.8	0.72	0.790269786	0.715554807	0.62	0.557552732
103	0	0.586927594	0.583964042	1	0.37654321	0.482433468	0.56	0.72	0.637858415	0.476600022	0.54	0.628648972
104	1	0.808197671	0.778435328	0	0.715517241	0.486532066	0.5	0.9	0.651100275	0.479986001	0.56	0.673407113
105	1	0.804268794	0.832437227	1	0.37654321	0.63487525	0.58	0.72	0.466291052	0.719809608	0.78	0.308126346
106	1	0.587735328	0.583078638	1	0.37654321	0.575128639	0.48	0.58	0.380583948	0.531426855	0.52	0.291734313
107	0	0.400757505	0.410351485	1	0.263157895	0.516288232	0.3	0.48	0.574858798	0.391032048	0.48	0.187082046
108	1	0.787868044	0.802672935	0	0.709677419	0.552489648	0.82	0.78	0.800182461	0.760078649	0.96	0.986641866
109	0	0.325885772	0.283362475	1	0.715517241	0.452810531	0.28	0.48	0.260638325	0.334869994	0.06	0.06470908
110	0	0.45262383	0.462437037	1	0.715517241	0.681818511	0.9	0.82	0.395608871	0.624231109	0.7	0.070553714
111	1	0.773696101	0.824222505	0	0.853932584	0.663238877	0.78	0.58	0	0.558838782	0.8	0.989559854
112	1	0.86233365	0.857895005	0	0.853932584	0.526473556	0.74	0.82	0.895936319	0.712239856	0.98	0.481898592
113	1	0.801959879	0.81452012	0	0.853932584	0.634077152	1	0.96	0.819110535	0.618659363	0.88	0.762558238
114	1	0.677568224	0.67821692	0	0.853932584	0.481911346	1	0.88	0.633672198	0.532890223	0.74	0.22470594
115	1	0.791007476	0.795510922	1	0.263157895	0.514084938	0.32	0.62	0.611681476	0.679105748	0.88	0.692841666
116	1	0.977570353	0.977213267	1	0.853932584	0.693674999	1	1	0.9949014	0.895620188	1	0.994624264
117	1	0.805046077	0.764634145	1	0.853932584	0.635305205	0.98	0.9	0.914750169	0.646536215	0.88	0.724076937
118	1	0.885115101	0.871758916	1	0.715517241	0.671769062	0.94	0.92	0.849099261	0.663567086	0.96	0.529861086
119	0	0.812562654	0.802352838	1	0.715517241	0.647501108	0.86	0.82	0.932666988	0.621680747	0.84	0.926270897
120	1	0.832452604	0.838550787	0	0.37654321	0.723959085	0.42	0.78	0.599819144	0.781699804	0.92	0.211114045
121	1	0.794042528	0.790105006	1	0.853932584	0.614853222	0.96	0.92	0.860718149	0.811896981	0.96	0.975339327
122	0	0.469628219	0.480971656	1	0.853932584	0.604501623	0.9	0.68	0.519004778	0.677120397	0.66	0.726042653
123	0	0.506366981	0.468913193	1	0.715517241	0.437104019	0.46	0.64	0.650521387	0.319027806	0.18	0.496197179
124	1	0.526128084	0.577672287	0	0.853932584	0.693974834	0.52	0.6	0.763436661	0.696299624	0.88	0.490809247
125	1	0.931136867	0.932196305	1	0.853932584	0.66478674	1	0.98	0.963817168	0.796607109	0.98	0.942237946
126	1	0.966870321	0.9643077	1	0.853932584	0.761257392	1	0.98	0.987055023	0.880299536	1	0.985557423
127	0	0.894754728	0.869741069	1	0.853932584	0.624053747	0.96	0.9	0.895424141	0.798105632	0.98	0.777523542
128	0	0.325329788	0.351066006	0	0.715517241	0.413300379	0.42	0.58	0.300729697	0.342018367	0.36	0.471849743
129	1	0.957334683	0.952145451	1	0.853932584	0.606957312	1	0.96	0.975871633	0.696688129	0.96	0.982018972
130	1	0.991618469	0.99176548	1	0.853932584	0.659103114	1	1	0.997490273	0.922486302	1	0.999776481
131	0	0.422006783	0.409274091	1	0.263157895	0.487724126	0.26	0.56	0.200541074	0.363668304	0.42	0.275377447
132	1	0.368709864	0.355809218	0	0.715517241	0.559599285	0.9	0.64	0.599907297	0.478165201	0.38	0.775021258
133	1	0.043534603	0.060244891	1	0.709677419	0.419830339	0.42	0.28	0.041353987	0.349265239	0.2	0.113536913
134	1	0.811604848	0.80082132	1	0.853932584	0.583609538	1	0.94	0.848871053	0.662532341	0.8	0.329234048
135	1	0.930745322	0.930676986	1	0.853932584	0.644842067	0.94	0.98	0.95797604	0.844168605	1	0.949692477
136	0	0.578443756	0.607343525	1	0.709677419	0.552795722	0.7	0.56	0.763147519	0.612043849	0.56	0.352424126
137	0	0.264788209	0.26712616	0	0.263157895	0.445479605	0.24	0.46	0.148021549	0.561444769	0.36	0.629980574
138	0	0.228048532	0.222718727	1	0.263157895	0.460703169	0.32	0.5	0.072524491	0.243578092	0.14	0.173161058
139	1	0.947931831	0.946246582	1	0.853932584	0.657243172	0.9	0.94	0.935691831	0.757459324	0.96	0.804041076
140	1	0.953007779	0.948607305	0	0.853932584	0.511179278	0.98	0.98	0.984196509	0.814145344	1	0.994034747
141	0	0.288509125	0.278563425	0	0.715517241	0.438610794	0.46	0.72	0.260125045	0.418513428	0.42	0.069653847
142	1	0.987587223	0.987660633	1	0.853932584	0.659282283	1	1	0.992943151	0.958941528	1	0.997076947
143	0	0.106725407	0.122506077	1	0.37654321	0.485225606	0.16	0.36	0.295592733	0.29761504	0.06	0.004660675
144	1	0.603317504	0.603935924	1	0.37654321	0.563084474	0.58	0.7	0.582342588	0.377040846	0.34	0.04427247
145	1	0.528244426	0.571966796	1	0.709677419	0.528367568	0.74	0.74	0.537006107	0.541948908	0.58	0.508179497
146	1	0.625228473	0.603045143	0	0.715517241	0.435836	0.42	0.72	0.789456082	0.404425392	0.46	0.211995345
147	1	0.855301933	0.850568463	1	0.853932584	0.661638865	0.84	0.92	0.796376452	0.676345014	0.78	0.802195477
148	0	0.330970634	0.323461186	1	0.715517241	0.446449231	0.18	0.4	0.410805966	0.566194655	0.32	0.726341223
149	1	0.978862208	0.974964245	0.5	0.853932584	0.666590718	0.98	1	0.984628725	0.920500126	1	0.971628285
150	1	0.541765104	0.563089365	1	0.853932584	0.544508886	0.76	0.46	0.888995985	0.519073279	0.64	0.388652017
151	1	0.955607769	0.953171458	1	0.853932584	0.733620478	1	1	0.965049937	0.818267605	1	0.983403754
152	1	0.849894608	0.851559517	0	0.715517241	0.402504258	0.52	0.84	0.787826821	0.475447183	0.76	0.849585987
153	1	0.439851283	0.431512773	1	0.37654321	0.465107519	0.36	0.54	0.438503402	0.261917624	0.12	0.079080687
154	1	0.682293546	0.699130241	1	0.715517241	0.469764684	0.4	0.68	0.801339642	0.475195904	0.46	0.785907603
155	1	0.941821751	0.953267509	1	0.853932584	0.612729455	0.94	0.72	0.960993451	0.860862994	0.98	0.871346357
156	0	0.606644921	0.654751	0	0.37654321	0.537763889	0.36	0.64	0.843424601	0.673782794	0.86	0.554761734
157	1	0.577743041	0.535185546	1	0.715517241	0.550667051	0.46	0.7	0.763176101	0.440004006	0.16	0.402775535
158	1	0.940571742	0.932670352	1	0.853932584	0.661937905	1	0.92	0.863865865	0.665875344	0.94	0.674533101
159	1	0.82799209	0.794827622	1	0.853932584	0.542569242	0.96	0.98	0.899440655	0.636770584	0.84	0.710339956
160	1	0.914983032	0.896559997	0	0.853932584	0.540011832	1	0.94	0.940429168	0.753537621	0.98	0.754239443
161	1	0.867608341	0.865562821	0	0.715517241	0.608754473	0.54	0.84	0.91542566	0.785313261	0.98	0.954449629
162	1	0.924486061	0.908485462	0	0.853932584	0.565194376	1	1	0.794972132	0.741626137	0.88	0.845790684
163	0	0.559826763	0.561170764	0	0.715517241	0.500316116	0.8	0.8	0.676532556	0.370489428	0.44	0.144572183
164	1	0.666885321	0.650398206	0	0.853932584	0.540505427	1	0.94	0.898726472	0.659307197	0.74	0.757939683
165	1	0.436041988	0.464793478	0	0.853932584	0.407388494	0.32	0.4	0.856305805	0.314806242	0.48	0.07436155
166	1	0.231017737	0.24392182	0	0.263157895	0.399362705	0.14	0.44	0.055105881	0.362651418	0.28	0.088994609
167	1	0.906555777	0.908300092	1	0.715517241	0.678994558	0.66	0.84	0.982073986	0.793895786	0.98	0.971598048
168	1	0.916934553	0.9191646	1	0.853932584	0.636139012	1	0.92	0.907682553	0.740170066	0.9	0.947211018
169	1	0.958769081	0.956040058	1	0.853932584	0.724497732	1	1	0.959716801	0.821399336	1	0.997102533
170	1	0.961823541	0.963187213	1	0.853932584	0.515986995	0.86	0.92	0.780819852	0.701189674	0.92	0.979163096
171	1	0.940392518	0.934994971	1	0.853932584	0.559223473	0.86	0.86	0.967282633	0.779526993	0.96	0.930712449
172	0	0.536495895	0.519471933	0	0.715517241	0.551595309	0.82	0.7	0.294182932	0.313760084	0.4	0.463774159
173	0	0.554659732	0.573082885	0	0.263157895	0.496007638	0.3	0.46	0.370240712	0.562120058	0.7	0.549097912
174	0	0.036079816	0.049236513	1	0.37654321	0.369239451	0.08	0.18	0	0.328506954	0.02	0.1483859
175	1	0.652363036	0.678132544	1	0.853932584	0.648896274	0.8	0.9	0.591434086	0.59743354	0.92	0.759927177
176	0	0.829865188	0.809369304	1	0.715517241	0.520455253	0.76	0.78	0.873929866	0.573195856	0.9	0.711495649
177	1	0.977594976	0.979398074	0	0.853932584	0.739973676	1	0.98	0.981561846	0.882883582	1	0.989746792
178	1	0.272422216	0.284073455	1	0.709677419	0.52085877	0.44	0.36	0.043057899	0.468112138	0.18	0.022070755
179	1	0.863631829	0.852992832	1	0.715517241	0.616528154	0.88	0.96	0.904985097	0.597861203	0.58	0.700020142
180	1	0.431675019	0.456802238	1	0.37654321	0.464449462	0.22	0.34	0.091836968	0.650695274	0.6	0.502196627
181	1	0.861320607	0.863980527	0	0.715517241	0.612177323	0.94	0.9	0.911437187	0.654528861	0.84	0.701578565
182	1	0.47605473	0.500781482	0	0.715517241	0.603804876	0.74	0.62	0.37645494	0.698160655	0.66	0.560192778
183	1	0.933595227	0.9320626	0	0.853932584	0.686886547	1	0.96	0.98188858	0.813152871	0.98	0.488183894
184	1	0.956426081	0.958271733	1	0.715517241	0.550978708	0.96	0.94	0.865014171	0.791717835	0.98	0.96810436
185	1	0.930618158	0.932445843	0	0.853932584	0.612451628	1	0.96	0.90003725	0.49508432	0.98	0.980420057
186	1	0.887517047	0.876589798	1	0.853932584	0.602646134	0.9	0.96	0.91379379	0.68442872	0.88	0.292018733
187	1	0.987840975	0.993119394	1	0.853932584	0.675738453	0.82	0.84	0.990910395	0.902783787	1	0.998671825
188	0	0.314034464	0.321410728	1	0.715517241	0.610774465	0.7	0.7	0.281557598	0.437354275	0.28	0.204126729
189	1	0.993420917	0.995279181	1	0.853932584	0.702943424	1	0.98	0.997913237	0.942685379	1	0.999952756
190	0	0.268835714	0.292196344	1	0.37654321	0.487277278	0.34	0.42	0.258225234	0.45822571	0.28	0.460980254
191	1	0.828557536	0.816846867	0	0.853932584	0.591848791	0.84	0.82	0.875768671	0.739327928	0.92	0.857647029
192	1	0.891616775	0.883399134	1	0.853932584	0.583337505	1	0.96	0.966278427	0.781109578	0.98	0.867783762
193	0	0.903689218	0.898317242	1	0.853932584	0.689346256	0.96	0.96	0.888477694	0.718522163	0.96	0.992238761
194	1	0.49697998	0.516468221	1	0.715517241	0.524751545	0.68	0.7	0.314630831	0.457971519	0.44	0.070171807
195	0	0.400359906	0.410772852	1	0.37654321	0.392752825	0.12	0.3	0.292848778	0.402222157	0.16	0.059748558
196	1	0.927706207	0.924502052	1	0.853932584	0.606322877	1	0.96	0.958292227	0.795993997	0.98	0.983706586
197	1	0.761632708	0.757347601	1	0.37654321	0.498401786	0.26	0.52	0.677479437	0.637343116	0.78	0.47247389
198	1	0.967440927	0.970507253	1	0.853932584	0.742928581	1	0.96	0.993504341	0.880706724	1	0.861589019
199	0	0.805887133	0.798053636	0	0.37654321	0.523922081	0.7	0.88	0.654405409	0.651379109	0.74	0.94875117
200	1	0.364525412	0.333939151	1	0.715517241	0.595288656	0.42	0.68	0.214848235	0.519938034	0.44	0.448826912

Question to Answer

Among these 11 algorithms, which one must be picked as the best predictive analytics algorithm? Why? Calculations are not necessary. (Hint: For a bank, which error is more expensive, false positive or false negative?)