multilabel_confusion_matrix¶

In [ ]:

multilabel_confusion_matrix(y_true: str, 
                            y_score: str, 
                            input_relation: Union[str, vDataFrame],
                            labels: list)

Computes the Multi Label Confusion Matrix.

Parameters¶

Name	Type	Optional	Description
y_true	str	❌	Response column.
y_score	str	❌	Prediction.
input_relation	str / vDataFrame	❌	The relation used for scoring. This relation can be a view, table, or customized relation. For example, you could write: "(SELECT ... FROM ...) x" as long as an alias is given at the end of the relation.
labels	list	❌	List of the response column categories.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [139]:

from verticapy import vDataFrame
vDataFrame("example_classification")

	123 y_score Float	123 y_true Int	123 y_pred Int
1	0.261992636494471	0	0
2	0.271766949212011	0	0
3	0.281715565124816	0	0
4	0.287704603820825	0	0
5	0.293503745589547	0	0
6	0.294754738264003	0	0
7	0.299562949987817	1	0
8	0.302916327585091	0	0
9	0.304072718408127	0	0
10	0.305575345919111	1	0
11	0.310835669040619	0	0
12	0.310835669040619	1	0
13	0.314677498532068	1	0
14	0.314772537794701	0	0
15	0.317504850884067	0	0
16	0.321047513138065	0	0
17	0.324898318971236	0	0
18	0.327313634871091	0	0
19	0.328290953867949	0	0
20	0.329530097026026	0	0
21	0.330667481508048	0	0
22	0.331694822691007	0	0
23	0.334891289625077	0	0
24	0.339224975430521	0	0
25	0.340649411718069	0	0
26	0.340914231767138	1	0
27	0.34178321869448	0	0
28	0.342456823846109	0	0
29	0.342607538574227	0	0
30	0.343380249983169	1	0
31	0.343380249983169	1	0
32	0.345611509579921	0	0
33	0.345703353743199	0	0
34	0.346341459924855	1	0
35	0.346566377721986	0	0
36	0.34720574465618	0	0
37	0.347320286991246	0	0
38	0.347361879717578	0	0
39	0.347895646927998	0	0
40	0.350041368591872	0	0
41	0.350713668608726	0	0
42	0.351097328234269	1	0
43	0.352016830512644	0	0
44	0.352054220262656	0	0
45	0.352146855196749	0	0
46	0.352239501254866	0	0
47	0.353175137762404	0	0
48	0.3539395916197	1	0
49	0.353996333596281	1	0
50	0.354381624264835	0	0
51	0.354381624264835	0	0
52	0.355342565735203	0	0
53	0.357132185344874	0	0
54	0.357455496335166	0	0
55	0.357679708354009	0	0
56	0.358738033924215	0	0
57	0.359210075590932	0	0
58	0.359566016764265	0	0
59	0.359641574716634	0	0
60	0.36060286105325	0	0
61	0.36060286105325	0	0
62	0.36101075208364	0	0
63	0.362255481975375	0	0
64	0.363178572038573	0	0
65	0.363600418603227	0	0
66	0.364693844779539	0	0
67	0.364907992756202	0	0
68	0.365288848686146	0	0
69	0.367100035711998	0	0
70	0.367262588765524	0	0
71	0.367427213035335	0	0
72	0.368048294763488	0	0
73	0.369225730045058	0	0
74	0.369797716658633	0	0
75	0.370553858267955	0	0
76	0.370553858267955	1	0
77	0.370753825925793	0	0
78	0.37140040117994	1	0
79	0.37207963832586	0	0
80	0.37272228535973	1	0
81	0.37279074038996	0	0
82	0.37337254481298	0	0
83	0.373809604665009	0	0
84	0.373973373195992	1	0
85	0.373973437248784	0	0
86	0.374595381587019	1	0
87	0.376588338299031	0	0
88	0.376653483221958	0	0
89	0.377288922205616	0	0
90	0.378217229733463	0	0
91	0.378825744070643	0	0
92	0.378825744070643	0	0
93	0.378825744070643	1	0
94	0.378825744070643	1	0
95	0.37911132124581	0	0
96	0.380187437748189	0	0
97	0.38033983811501	0	0
98	0.380568066078983	0	0
99	0.380732995250046	0	0
100	0.380876617505906	0	0

Out[139]:

Rows: 1-100 of 1234 | Columns: 3

In [140]:

from verticapy.learn.metrics import multilabel_confusion_matrix
multilabel_confusion_matrix("y_true", 
                            "y_pred", 
                            "example_classification",
                            labels = [0, 1])

	0	1
0	484	300
1	194	256

Out[140]: