classification_report¶

In [ ]:

classification_report(y_true: str = "", 
                      y_score: list = [], 
                      input_relation: Union[str, vDataFrame] = "",
                      labels: list = [],
                      cutoff: Union[float, list] = [],
                      estimator = None,
                      nbins: int = 10000)

Computes a classification report using multiple metrics (AUC, accuracy, PRC AUC, F1...). It will consider each category as positive and switch to the next one during the computation.

Parameters¶

Name	Type	Optional	Description
y_true	str	✓	Response column.
y_score	list	✓	List containing the probability and the 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 to use.
cutoff	float / list	✓	Cutoff for which the tested category will be accepted as prediction. In case of multiclass classification, the list will represent the the classes threshold. If it is empty, the best cutoff will be used.
estimator	object	✓	Estimator to use to compute the classification report.
nbins	int	✓	[Used to compute ROC AUC, PRC AUC and the best cutoff] An integer value that determines the number of decision boundaries. Decision boundaries are set at equally spaced intervals between 0 and 1, inclusive. Greater values for nbins give more precise estimations of the metrics, but can potentially decrease performance. The maximum value is 999,999. If negative, the maximum value is used.

Returns¶

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

Example¶

In [110]:

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[110]:

Rows: 1-100 of 1234 | Columns: 3

In [111]:

from verticapy.learn.metrics import classification_report
classification_report("y_true", 
                      ["y_score", "y_pred"], 
                      "example_classification")

	value
auc	0.6974762740166146
prc_auc	0.6003540469187277
accuracy	0.5996758508914101
log_loss	0.281741002875517
precision	0.460431654676259
recall	0.5688888888888889
f1_score	0.5921281517022536
mcc	0.18016701318842082
informedness	0.18623582766439917
markedness	0.17429596146091964
csi	0.3413333333333333
cutoff	0.999

Out[111]: