verticapy.machine_learning.vertica.tsa.AR.features_importance#
- AR.features_importance(show: bool = True, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure #
Computes the model’s features importance.
Parameters#
- show: bool, optional
If set to
True
, draw the feature’s importance.- chart: PlottingObject, optional
The chart object to plot on.
- **style_kwargs
Any optional parameter to pass to the Plotting functions.
Returns#
- obj
features importance.
Examples#
We import
verticapy
:import verticapy as vp
For this example, we will use the airline passengers dataset.
import verticapy.datasets as vpd data = vpd.load_airline_passengers()
📅dateDate123passengersInteger1 1949-01-01 112 2 1949-02-01 118 3 1949-03-01 132 4 1949-04-01 129 5 1949-05-01 121 6 1949-06-01 135 7 1949-07-01 148 8 1949-08-01 148 9 1949-09-01 136 10 1949-10-01 119 11 1949-11-01 104 12 1949-12-01 118 13 1950-01-01 115 14 1950-02-01 126 15 1950-03-01 141 16 1950-04-01 135 17 1950-05-01 125 18 1950-06-01 149 19 1950-07-01 170 20 1950-08-01 170 21 1950-09-01 158 22 1950-10-01 133 23 1950-11-01 114 24 1950-12-01 140 25 1951-01-01 145 26 1951-02-01 150 27 1951-03-01 178 28 1951-04-01 163 29 1951-05-01 172 30 1951-06-01 178 31 1951-07-01 199 32 1951-08-01 199 33 1951-09-01 184 34 1951-10-01 162 35 1951-11-01 146 36 1951-12-01 166 37 1952-01-01 171 38 1952-02-01 180 39 1952-03-01 193 40 1952-04-01 181 41 1952-05-01 183 42 1952-06-01 218 43 1952-07-01 230 44 1952-08-01 242 45 1952-09-01 209 46 1952-10-01 191 47 1952-11-01 172 48 1952-12-01 194 49 1953-01-01 196 50 1953-02-01 196 51 1953-03-01 236 52 1953-04-01 235 53 1953-05-01 229 54 1953-06-01 243 55 1953-07-01 264 56 1953-08-01 272 57 1953-09-01 237 58 1953-10-01 211 59 1953-11-01 180 60 1953-12-01 201 61 1954-01-01 204 62 1954-02-01 188 63 1954-03-01 235 64 1954-04-01 227 65 1954-05-01 234 66 1954-06-01 264 67 1954-07-01 302 68 1954-08-01 293 69 1954-09-01 259 70 1954-10-01 229 71 1954-11-01 203 72 1954-12-01 229 73 1955-01-01 242 74 1955-02-01 233 75 1955-03-01 267 76 1955-04-01 269 77 1955-05-01 270 78 1955-06-01 315 79 1955-07-01 364 80 1955-08-01 347 81 1955-09-01 312 82 1955-10-01 274 83 1955-11-01 237 84 1955-12-01 278 85 1956-01-01 284 86 1956-02-01 277 87 1956-03-01 317 88 1956-04-01 313 89 1956-05-01 318 90 1956-06-01 374 91 1956-07-01 413 92 1956-08-01 405 93 1956-09-01 355 94 1956-10-01 306 95 1956-11-01 271 96 1956-12-01 306 97 1957-01-01 315 98 1957-02-01 301 99 1957-03-01 356 100 1957-04-01 348 Rows: 1-100 | Columns: 2First we import the model:
from verticapy.machine_learning.vertica.tsa import ARIMA
Then we can create the model:
model = ARIMA(order = (12, 1, 2))
We can now fit the model:
model.fit(data, "date", "passengers")
We can conveniently get the features importance:
model.features_importance() Out[5]: <Axes: xlabel='Importance (%)', ylabel='Features'>