vastorbit.machine_learning.metrics.critical_success_index¶

Computes the Critical Success Index.

Parameters:

y_true (str) – Response column.
y_score (str) – Prediction.
input_relation (SQLRelation) – Relation used 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
average (str, optional) –
The method used to compute the final score for multiclass-classification.
- binary:
  considers one of the classes as positive and use the binary confusion matrix to compute the score.
- micro:
  positive and negative values globally.
- macro:
  average of the score of each class.
- score:
  scores for all the classes.
- weighted :
  weighted average of the score of each class.
- None:
  accuracy.
If empty, the behaviour is similar to the ‘scores’ option.
labels (ArrayLike, optional) – List of the response column categories.
pos_label (PythonScalar, optional) – To compute the metric, one of the response column classes must be the positive class. The parameter ‘pos_label’ represents this class.

Returns:

score.

Return type:

float

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, 0, 0, 1],
        "y_pred": [1, 1, 1, 0, 1],
    },
)

Next, we import the metric:

from vastorbit.machine_learning.metrics import critical_success_index

Now we can conveniently calculate the score:

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

Note

For multi-class classification, we can select the average method for averaging from the following options: - binary - micro - macro - scores - weighted

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

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

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.