vastorbit.machine_learning.metrics.aic_score¶

vastorbit.machine_learning.metrics.aic_score(y_true: str, y_score: str, input_relation: Annotated[str | VastFrame, ''], k: int = 1) → float¶

Returns the AIC score.

Parameters:

y_true (str) – Response column.
y_score (str) – Prediction.
input_relation (SQLRelation) – Relation to use for scoring. This relation can be a view, table, or a customized relation (if an alias is used at the end of the relation). For example: (SELECT … FROM …) x
k (int, optional) – Number of predictors.

Examples

We should first import vastorbit.

import vastorbit as vo

Let’s create a small dataset that has:

true value
predicted value

data = vo.VastFrame(
    {
        "y_true": [1, 1.5, 3, 2, 5],
        "y_pred": [1.1, 1.55, 2.9, 2.01, 4.5],
    }
)

Next, we import the metric:

from vastorbit.machine_learning.metrics import aic_score

Now we can conveniently calculate the score:

aic_score(
    y_true = "y_true",
    y_score = "y_pred",
    input_relation = data,
)

It is also possible to directly compute the score from the VastFrame:

data.score(
    y_true  = "y_true",
    y_score = "y_pred",
    metric  = "aic",
)

Note

vastorbit uses simple SQL queries to compute various metrics. You can use the set_option() function with the sql_on parameter to enable SQL generation and examine the generated queries.