verticapy.machine_learning.metrics.explained_variance#
- verticapy.machine_learning.metrics.explained_variance(y_true: str, y_score: str, input_relation: str | vDataFrame) float #
Computes the Explained Variance.
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
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 explained_variance
Now we can conveniently calculate the score:
explained_variance( y_true = "y_true", y_score = "y_pred", input_relation = data, ) Out[4]: 0.976612
It is also possible to directly compute the score from the vDataFrame:
data.score( y_true = "y_true", y_score = "y_pred", metric = "explained_variance", ) Out[5]: 0.976612
Note
VerticaPy uses simple SQL queries to compute various metrics. You can use the
set_option()
function with thesql_on
parameter to enable SQL generation and examine the generated queries.See also
vDataFrame.
score()
: Computes the input ML metric.