verticapy.machine_learning.metrics.quantile_error#

verticapy.machine_learning.metrics.quantile_error(y_true: str, y_score: str, input_relation: str | vDataFrame, q: int | float | Decimal) → float#

Computes the input quantile of the Error.

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
q: PythonNumber: Input quantile.

Returns#

float: score.

Examples#

We should first import verticapy.

import verticapy as vp

Let’s create a small dataset that has:

true value
predicted value

data = vp.vDataFrame(
    {
        "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 verticapy.machine_learning.metrics import quantile_error

Now we can conveniently calculate the score:

quantile_error(
    y_true  = "y_true",
    y_score = "y_pred",
    input_relation = data,
    q = 0.25, # First Quartile
)

Out[4]: 0.05

Note

VerticaPy 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.