prc_curve¶

In [ ]:

prc_curve(y_true: str, 
          y_score: str,
          input_relation: (str, vDataFrame),
          pos_label: (int, float, str) = 1,
          nbins: int = 30,
          auc_prc: bool = False,
          ax=None,
          **style_kwds,)

Draws the PRC Curve.

Parameters¶

Name	Type	Optional	Description
y_true	str	❌	Response column.
y_score	str	❌	Prediction Probability.
input_relation	str / vDataFrame	❌	Relation to use to do the scoring. The relation can be a view or a table or even a customized relation. For example, you could write: "(SELECT ... FROM ...) x" as long as an alias is given at the end of the relation.
pos_label	int / float / str	✓	To compute the PRC Curve, one of the response column class has to be the positive one. The parameter 'pos_label' represents this class.
nbins	int	✓	Curve number of bins.
auc_prc	bool	✓	If set to True, the function will return the PRC AUC without drawing the curve.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

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

Example¶

In [7]:

from verticapy import vDataFrame
vDataFrame("example_classification")

Out[7]:

	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

Rows: 1-100 | Columns: 3

In [8]:

from verticapy.learn.model_selection import prc_curve
prc_curve("y_true", "y_score", "example_classification")

Out[8]:

	threshold	recall	precision
1	0	1	0
2	0.0	1.0	0.392570281124498
3	0.0333333333333333	1.0	0.392570281124498
4	0.0666666666666667	1.0	0.392570281124498
5	0.1	1.0	0.392570281124498
6	0.133333333333333	1.0	0.392570281124498
7	0.166666666666667	1.0	0.392570281124498
8	0.2	1.0	0.392570281124498
9	0.233333333333333	1.0	0.392570281124498
10	0.266666666666667	1.0	0.392964824120603
11	0.3	0.997442455242967	0.394337714863499
12	0.333333333333333	0.989769820971867	0.397330595482546
13	0.366666666666667	0.971867007672634	0.40948275862069
14	0.4	0.907928388746803	0.43558282208589
15	0.433333333333333	0.723785166240409	0.491319444444444
16	0.466666666666667	0.578005115089514	0.585492227979275
17	0.5	0.50383631713555	0.619496855345912
18	0.533333333333333	0.432225063938619	0.637735849056604
19	0.566666666666667	0.347826086956522	0.660194174757282
20	0.6	0.273657289002558	0.660493827160494
21	0.633333333333333	0.232736572890026	0.684210526315789
22	0.666666666666667	0.191815856777494	0.688073394495413
23	0.7	0.166240409207161	0.755813953488372
24	0.733333333333333	0.148337595907928	0.743589743589744
25	0.766666666666667	0.132992327365729	0.732394366197183
26	0.8	0.107416879795396	0.711864406779661
27	0.833333333333333	0.0869565217391304	0.666666666666667
28	0.866666666666667	0.0767263427109974	0.714285714285714
29	0.9	0.0664961636828645	0.702702702702703
30	0.933333333333333	0.0562659846547315	0.758620689655172
31	0.966666666666667	0.0255754475703325	0.833333333333333
32	1	0	1

Rows: 1-32 | Columns: 3