explained_variance¶

In [ ]:

explained_variance(y_true: str, 
                   y_score: str, 
                   input_relation: Union[str, vDataFrame)]

Computes the Explained Variance.

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.

Returns¶

float : score

Example¶

In [116]:

from verticapy import vDataFrame
vDataFrame("regression_example")

	123 y_score Float	123 y_true Int
1	4.83230828816342	5
2	4.90973865471403	6
3	4.93581571039053	3
4	4.94753529556305	3
5	4.97599820696914	5
6	4.98252235135235	3
7	5.00568284510684	5
8	5.02421149663807	5
9	5.02421149663807	5
10	5.06408311293723	6
11	5.07593720361093	5
12	5.07856396920139	6
13	5.08137723147877	4
14	5.08436497019489	4
15	5.08485336002691	6
16	5.08487975289775	6
17	5.08555627211573	5
18	5.09128285038509	4
19	5.09503037596897	5
20	5.10545755454676	5
21	5.10617758398426	5
22	5.11060857109918	6
23	5.11157184429775	4
24	5.12173944281158	5
25	5.12749070568114	5
26	5.12789348999283	5
27	5.12940317280752	5
28	5.13105519897955	5
29	5.13530025694603	6
30	5.1379184524885	6
31	5.1379184524885	6
32	5.14252730418092	5
33	5.1436253577583	5
34	5.14431694980633	8
35	5.14601878470978	5
36	5.14917823139756	3
37	5.15404688093427	6
38	5.16278390433579	5
39	5.1661846779708	4
40	5.1725345636956	5
41	5.17360452536863	5
42	5.17562014596056	5
43	5.1761022415537	6
44	5.1761022415537	6
45	5.17643222660313	5
46	5.17678187472389	4
47	5.17717072197356	5
48	5.17842739811385	6
49	5.17842739811385	6
50	5.17919716567529	5
51	5.17919716567529	5
52	5.18032309337668	5
53	5.18232789085881	5
54	5.18422068994976	6
55	5.18422068994976	6
56	5.18433869846503	6
57	5.18538701391847	6
58	5.18538701391847	6
59	5.18538701391847	6
60	5.1858708085451	6
61	5.1858708085451	6
62	5.18643532937191	6
63	5.18643532937191	6
64	5.18712288948001	5
65	5.18997506677153	6
66	5.19134515194868	7
67	5.19134515194868	7
68	5.19230170458485	6
69	5.19399067185988	5
70	5.19408306855008	5
71	5.19550091794003	5
72	5.19649419458997	5
73	5.19711418451414	5
74	5.19732664377804	5
75	5.19881049698735	5
76	5.19958677160387	6
77	5.20094943601124	6
78	5.20169755735308	6
79	5.20190797179569	5
80	5.20423708472417	4
81	5.20588607217004	4
82	5.2077498659875	5
83	5.2078249275487	5
84	5.20819191079845	7
85	5.20837721034699	4
86	5.20886185662689	5
87	5.20886185662689	5
88	5.21014697015664	6
89	5.21079872920281	5
90	5.21159184201735	5
91	5.21159184201735	5
92	5.21160889303156	4
93	5.2120801655149	5
94	5.21471464026295	5
95	5.21486095846289	6
96	5.21514551647898	6
97	5.21579897455865	6
98	5.21603039638381	4
99	5.21702104677539	5
100	5.21811450514739	6

Out[116]:

Rows: 1-100 of 6497 | Columns: 2

In [117]:

from verticapy.learn.metrics import explained_variance
explained_variance("y_true", "y_score", "regression_example")

Out[117]:

0.214960768970105