vastorbit.machine_learning.vast.ensemble.RandomForestClassifier.lift_chart

RandomForestClassifier.lift_chart(pos_label: Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] | None = None, nbins: int = 1000, show: bool = True, chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, **style_kwargs) → TableSample

Draws the model Lift Chart.

Parameters:

• pos_label (PythonScalar, optional) – To draw the Lift chart, one of the response column classes must be the positive class. The parameter pos_label represents this class.

• nbins (int, optional) – An integer value that determines the number of decision boundaries. Decision boundaries are set at equally-spaced intervals between 0 and 1, inclusive.

• show (bool, optional) – If set to True, the Plotting object is returned.

• chart (PlottingObject, optional) – The chart object to plot on.

• **style_kwargs – Any optional parameter to pass to the Plotting functions.

Returns:

lift chart data points.

Return type:

TableSample

Examples

For this example, we will use the Iris dataset.

import vastorbit.datasets as vod

data = vod.load_iris()

	123 sepallengthcm Decimal(5, 2)	123 sepalwidthcm Decimal(5, 2)	123 petallengthcm Decimal(5, 2)	123 petalwidthcm Decimal(5, 2)	Abc species Varchar(30)
1	5.1	3.5	1.4	0.2	Iris-setosa
2	4.9	3.0	1.4	0.2	Iris-setosa
3	4.7	3.2	1.3	0.2	Iris-setosa
4	4.6	3.1	1.5	0.2	Iris-setosa
5	5.0	3.6	1.4	0.2	Iris-setosa
6	5.4	3.9	1.7	0.4	Iris-setosa
7	4.6	3.4	1.4	0.3	Iris-setosa
8	5.0	3.4	1.5	0.2	Iris-setosa
9	4.4	2.9	1.4	0.2	Iris-setosa
10	4.9	3.1	1.5	0.1	Iris-setosa
11	5.4	3.7	1.5	0.2	Iris-setosa
12	4.8	3.4	1.6	0.2	Iris-setosa
13	4.8	3.0	1.4	0.1	Iris-setosa
14	4.3	3.0	1.1	0.1	Iris-setosa
15	5.8	4.0	1.2	0.2	Iris-setosa
16	5.7	4.4	1.5	0.4	Iris-setosa
17	5.4	3.9	1.3	0.4	Iris-setosa
18	5.1	3.5	1.4	0.3	Iris-setosa
19	5.7	3.8	1.7	0.3	Iris-setosa
20	5.1	3.8	1.5	0.3	Iris-setosa
21	5.4	3.4	1.7	0.2	Iris-setosa
22	5.1	3.7	1.5	0.4	Iris-setosa
23	4.6	3.6	1.0	0.2	Iris-setosa
24	5.1	3.3	1.7	0.5	Iris-setosa
25	4.8	3.4	1.9	0.2	Iris-setosa
26	5.0	3.0	1.6	0.2	Iris-setosa
27	5.0	3.4	1.6	0.4	Iris-setosa
28	5.2	3.5	1.5	0.2	Iris-setosa
29	5.2	3.4	1.4	0.2	Iris-setosa
30	4.7	3.2	1.6	0.2	Iris-setosa
31	4.8	3.1	1.6	0.2	Iris-setosa
32	5.4	3.4	1.5	0.4	Iris-setosa
33	5.2	4.1	1.5	0.1	Iris-setosa
34	5.5	4.2	1.4	0.2	Iris-setosa
35	4.9	3.1	1.5	0.1	Iris-setosa
36	5.0	3.2	1.2	0.2	Iris-setosa
37	5.5	3.5	1.3	0.2	Iris-setosa
38	4.9	3.1	1.5	0.1	Iris-setosa
39	4.4	3.0	1.3	0.2	Iris-setosa
40	5.1	3.4	1.5	0.2	Iris-setosa
41	5.0	3.5	1.3	0.3	Iris-setosa
42	4.5	2.3	1.3	0.3	Iris-setosa
43	4.4	3.2	1.3	0.2	Iris-setosa
44	5.0	3.5	1.6	0.6	Iris-setosa
45	5.1	3.8	1.9	0.4	Iris-setosa
46	4.8	3.0	1.4	0.3	Iris-setosa
47	5.1	3.8	1.6	0.2	Iris-setosa
48	4.6	3.2	1.4	0.2	Iris-setosa
49	5.3	3.7	1.5	0.2	Iris-setosa
50	5.0	3.3	1.4	0.2	Iris-setosa
51	7.0	3.2	4.7	1.4	Iris-versicolor
52	6.4	3.2	4.5	1.5	Iris-versicolor
53	6.9	3.1	4.9	1.5	Iris-versicolor
54	5.5	2.3	4.0	1.3	Iris-versicolor
55	6.5	2.8	4.6	1.5	Iris-versicolor
56	5.7	2.8	4.5	1.3	Iris-versicolor
57	6.3	3.3	4.7	1.6	Iris-versicolor
58	4.9	2.4	3.3	1.0	Iris-versicolor
59	6.6	2.9	4.6	1.3	Iris-versicolor
60	5.2	2.7	3.9	1.4	Iris-versicolor
61	5.0	2.0	3.5	1.0	Iris-versicolor
62	5.9	3.0	4.2	1.5	Iris-versicolor
63	6.0	2.2	4.0	1.0	Iris-versicolor
64	6.1	2.9	4.7	1.4	Iris-versicolor
65	5.6	2.9	3.6	1.3	Iris-versicolor
66	6.7	3.1	4.4	1.4	Iris-versicolor
67	5.6	3.0	4.5	1.5	Iris-versicolor
68	5.8	2.7	4.1	1.0	Iris-versicolor
69	6.2	2.2	4.5	1.5	Iris-versicolor
70	5.6	2.5	3.9	1.1	Iris-versicolor
71	5.9	3.2	4.8	1.8	Iris-versicolor
72	6.1	2.8	4.0	1.3	Iris-versicolor
73	6.3	2.5	4.9	1.5	Iris-versicolor
74	6.1	2.8	4.7	1.2	Iris-versicolor
75	6.4	2.9	4.3	1.3	Iris-versicolor
76	6.6	3.0	4.4	1.4	Iris-versicolor
77	6.8	2.8	4.8	1.4	Iris-versicolor
78	6.7	3.0	5.0	1.7	Iris-versicolor
79	6.0	2.9	4.5	1.5	Iris-versicolor
80	5.7	2.6	3.5	1.0	Iris-versicolor
81	5.5	2.4	3.8	1.1	Iris-versicolor
82	5.5	2.4	3.7	1.0	Iris-versicolor
83	5.8	2.7	3.9	1.2	Iris-versicolor
84	6.0	2.7	5.1	1.6	Iris-versicolor
85	5.4	3.0	4.5	1.5	Iris-versicolor
86	6.0	3.4	4.5	1.6	Iris-versicolor
87	6.7	3.1	4.7	1.5	Iris-versicolor
88	6.3	2.3	4.4	1.3	Iris-versicolor
89	5.6	3.0	4.1	1.3	Iris-versicolor
90	5.5	2.5	4.0	1.3	Iris-versicolor
91	5.5	2.6	4.4	1.2	Iris-versicolor
92	6.1	3.0	4.6	1.4	Iris-versicolor
93	5.8	2.6	4.0	1.2	Iris-versicolor
94	5.0	2.3	3.3	1.0	Iris-versicolor
95	5.6	2.7	4.2	1.3	Iris-versicolor
96	5.7	3.0	4.2	1.2	Iris-versicolor
97	5.7	2.9	4.2	1.3	Iris-versicolor
98	6.2	2.9	4.3	1.3	Iris-versicolor
99	5.1	2.5	3.0	1.1	Iris-versicolor
100	5.7	2.8	4.1	1.3	Iris-versicolor

Rows: 1-100 | Columns: 5

Let’s import the model:

from vastorbit.machine_learning.vast import NearestCentroid

Then we can create the model:

model = NearestCentroid(p = 2)

We can now fit the model:

model.fit(
    data,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "Species",
)

To get the Lift chart:

model.lift_chart(pos_label= "Iris-setosa")

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.