lift_chart¶

In [ ]:

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

Draws a Lift Chart.

Parameters¶

Name	Type	Optional	Description
y_true	str	❌	Response column.
y_score	str	❌	Prediction Probability.
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.
pos_label	int / float / str	✓	To compute the Lift Chart, 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.
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 [15]:

from verticapy import vDataFrame
vDataFrame("example_classification")

Out[15]:

	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 [16]:

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

Out[16]:

	decision_boundary	positive_prediction_ratio	lift
1	0.0	0.0	2.1227621483376
2	0.0333333333333333	0.0255754475703325	2.1227621483376
3	0.0666666666666667	0.0562659846547315	1.93244554193491
4	0.1	0.0664961636828645	1.79000483859819
5	0.133333333333333	0.0767263427109974	1.8195104128608
6	0.166666666666667	0.0869565217391304	1.69820971867008
7	0.2	0.107416879795396	1.81334258095279
8	0.233333333333333	0.132992327365729	1.8656388458629
9	0.266666666666667	0.148337595907928	1.89415699390124
10	0.3	0.166240409207161	1.92529590198061
11	0.333333333333333	0.191815856777494	1.75273938853563
12	0.366666666666667	0.232736572890026	1.74289944810876
13	0.4	0.273657289002558	1.68248555460832
14	0.433333333333333	0.347826086956522	1.68172224567328
15	0.466666666666667	0.432225063938619	1.62451382521836
16	0.5	0.50383631713555	1.57805337065097
17	0.533333333333333	0.578005115089514	1.4914328876403
18	0.566666666666667	0.723785166240409	1.25154518329071
19	0.6	0.907928388746803	1.10956647262799
20	0.633333333333333	0.971867007672634	1.04308140047623
21	0.666666666666667	0.989769820971867	1.01212601816014
22	0.7	0.997442455242967	1.00450220972901
23	0.733333333333333	1.0	1.00100502512563
24	0.766666666666667	1.0	1.0
25	0.8	1.0	1.0
26	0.833333333333333	1.0	1.0
27	0.866666666666667	1.0	1.0
28	0.9	1.0	1.0
29	0.933333333333333	1.0	1.0
30	0.966666666666667	1.0	1.0
31	1.0	1.0	1.0

Rows: 1-31 | Columns: 3