Feedback

×

• Feedback
• Was this page helpful?*
  • Yes
  • No
• Do you have any additional feedback?
  • Yes
  • No
• Additional Feedback

verticapy.machine_learning.vertica.ensemble.IsolationForest.predict

IsolationForest.predict(vdf: Annotated[str | vDataFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str | None = None, cutoff: Annotated[int | float | Decimal, 'Python Numbers'] = 0.7, contamination: Annotated[int | float | Decimal, 'Python Numbers'] | None = None, inplace: bool = True) → vDataFrame

Predicts using the input relation.

Parameters

vdf: SQLRelation

Object to use for the prediction. You can specify a customized relation if it is enclosed with an alias. For example, (SELECT 1) x is valid, whereas (SELECT 1) and SELECT 1 are invalid.

X: list, optional

list of columns used to deploy the models. If empty, the model predictors are used.

name: str, optional

Name of the additional vDataColumn. If empty, a name is generated.

cutoff: PythonNumber, optional

float in the range (0.0, 1.0), specifies the threshold that determines if a data point is an anomaly. If the anomaly_score for a data point is greater than or equal to the cutfoff, the data point is marked as an anomaly.

contamination: PythonNumber, optional

float in the range (0, 1), the approximate ratio of data points in the training data that should be labeled as anomalous. If this parameter is specified, the cutoff parameter is ignored.

inplace: bool, optional

If True, the prediction is added to the vDataFrame.

Returns

vDataFrame

the input object.

Examples

We import verticapy:

import verticapy as vp

For this example, we will use the winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
3	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
4	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
5	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
6	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
7	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
8	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
9	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
10	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
11	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
12	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
13	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
14	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
15	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
16	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
17	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
18	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
19	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
20	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
21	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
30	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
31	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
45	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
71	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
72	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
73	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
81	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
82	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
83	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	8	1	white

Rows: 1-100 | Columns: 14

We import the IsolationForest model:

from verticapy.machine_learning.vertica import IsolationForest

Then we can create the model:

model = IsolationForest(
    n_estimators = 10,
    max_depth = 3,
    nbins = 6,
)

We can now fit the model:

model.fit(data, X = ["density", "sulphates"])


===========
call_string
===========
SELECT iforest('"public"."_verticapy_tmp_isolationforest_v_mldb_f299371c979711efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_f2cb47e8979711efa8720242ac120002_"', '"density", "sulphates"' USING PARAMETERS exclude_columns='', ntree=10, sampling_size=0.632, col_sample_by_tree=1, max_depth=3, nbins=6);

=======
details
=======
predictor|      type      
---------+----------------
 density |float or numeric
sulphates|float or numeric


===============
Additional Info
===============
       Name       |Value
------------------+-----
    tree_count    | 10  
rejected_row_count|  0  
accepted_row_count|6497 

Prediction is straight-forward:

model.predict(data, ["density", "sulphates"])

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 Integer
1	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white
2	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white
3	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white
4	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red
5	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white
6	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5	0	white
7	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white
8	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white
9	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
10	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
11	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8	1	white
12	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white
13	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red
14	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white
15	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white
16	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white
17	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white
18	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white
19	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6	0	white
20	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
21	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
22	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
23	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red
24	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red
25	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white
26	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white
27	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white
28	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6	0	white
29	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red
30	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red
31	5.2	0.405	0.15	1.45	0.038	10.0	44.0	0.99125	3.52	0.4	11.6	4	0	white
32	5.2	0.6	0.07	7.0	0.044	33.0	147.0	0.9944	3.33	0.58	9.7	5	0	white
33	5.2	0.645	0.0	2.15	0.08	15.0	28.0	0.99444	3.78	0.61	12.5	6	0	red
34	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white
35	5.3	0.24	0.33	1.3	0.033	25.0	97.0	0.9906	3.59	0.38	11.0	8	1	white
36	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white
37	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white
38	5.3	0.395	0.07	1.3	0.035	26.0	102.0	0.992	3.5	0.35	10.6	6	0	white
39	5.3	0.43	0.11	1.1	0.029	6.0	51.0	0.99076	3.51	0.48	11.2	4	0	white
40	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white
41	5.3	0.585	0.07	7.1	0.044	34.0	145.0	0.9945	3.34	0.57	9.7	6	0	white
42	5.3	0.76	0.03	2.7	0.043	27.0	93.0	0.9932	3.34	0.38	9.2	5	0	white
43	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white
44	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white
45	5.4	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	6	0	white
46	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white
47	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white
48	5.4	0.45	0.27	6.4	0.033	20.0	102.0	0.98944	3.22	0.27	13.4	8	1	white
49	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white
50	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white
51	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white
52	5.4	0.59	0.07	7.0	0.045	36.0	147.0	0.9944	3.34	0.57	9.7	6	0	white
53	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white
54	5.5	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white
55	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white
56	5.5	0.24	0.32	8.7	0.06	19.0	102.0	0.994	3.27	0.31	10.4	5	0	white
57	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white
58	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white
59	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white
60	5.6	0.18	0.31	1.5	0.038	16.0	84.0	0.9924	3.34	0.58	10.1	6	0	white
61	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white
62	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white
63	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white
64	5.6	0.19	0.31	2.7	0.027	11.0	100.0	0.98964	3.46	0.4	13.2	7	1	white
65	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white
66	5.6	0.2	0.66	10.2	0.043	78.0	175.0	0.9945	2.98	0.43	10.4	7	1	white
67	5.6	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white
68	5.6	0.225	0.24	9.8	0.054	59.0	140.0	0.99545	3.17	0.39	10.2	6	0	white
69	5.6	0.23	0.25	8.0	0.043	31.0	101.0	0.99429	3.19	0.42	10.4	6	0	white
70	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white
71	5.6	0.235	0.29	1.2	0.047	33.0	127.0	0.991	3.34	0.5	11.0	7	1	white
72	5.6	0.27	0.37	0.9	0.025	11.0	49.0	0.98845	3.29	0.33	13.1	6	0	white
73	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white
74	5.6	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white
75	5.6	0.295	0.2	2.2	0.049	18.0	134.0	0.99378	3.21	0.68	10.0	5	0	white
76	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red
77	5.6	0.33	0.28	1.2	0.031	33.0	97.0	0.99126	3.49	0.58	10.9	6	0	white
78	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white
79	5.6	0.34	0.25	2.5	0.046	47.0	182.0	0.99093	3.21	0.4	11.3	5	0	white
80	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white
81	5.6	0.41	0.22	7.1	0.05	44.0	154.0	0.9931	3.3	0.4	10.5	5	0	white
82	5.6	0.46	0.24	4.8	0.042	24.0	72.0	0.9908	3.29	0.37	12.6	6	0	white
83	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red
84	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red
85	5.6	0.605	0.05	2.4	0.073	19.0	25.0	0.99258	3.56	0.55	12.9	5	0	red
86	5.6	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white
87	5.7	0.135	0.3	4.6	0.042	19.0	101.0	0.9946	3.31	0.42	9.3	6	0	white
88	5.7	0.14	0.3	5.4	0.045	26.0	105.0	0.99469	3.32	0.45	9.3	5	0	white
89	5.7	0.18	0.22	4.2	0.042	25.0	111.0	0.994	3.35	0.39	9.4	5	0	white
90	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white
91	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white
92	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white
93	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white
94	5.7	0.22	0.28	1.3	0.027	26.0	101.0	0.98948	3.35	0.38	12.5	7	1	white
95	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white
96	5.7	0.25	0.21	1.5	0.044	21.0	108.0	0.99142	3.3	0.59	11.0	6	0	white
97	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white
98	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white
99	5.7	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white
100	5.7	0.31	0.29	7.3	0.05	33.0	143.0	0.99332	3.31	0.5	11.0666666666667	6	0	white

Rows: 1-100 | Columns: 15

Note

Refer to IsolationForest for more information about the different methods and usages.