get_fnr_curve

Return points of the FNR curve.

Method call format

get_fnr_curve(model=None,
              data=None,
              curve=None,
              thread_count=-1,
              plot=False)

Parameters

model

Description

The trained model.

Possible types

catboost.CatBoost

Default value

None

data

Description

A set of samples to build the FNR curve with.

Should not be used with the curve parameter.

Possible types

• catboost.Pool
• list of catboost.Pool

Default value

None

curve

Description

ROC curve points.

Should not be used with the data parameter.

Required if the data and model parameters are set to None.

It is strictly recommended to use the output of the get_roc_curve function as the value of this parameter.

The input data must certain criteria:

• The threshold values should not increase.
• There should not be any repetitions of the fpr-tpr- threshold triplets.

Possible types

tuple of three arrays (fpr, tpr, thresholds)

Default value

None

thread_count

Description

The number of threads to use.

Optimizes the speed of execution. This parameter doesn't affect results.

Possible types

int

Default value

-1 (the number of threads is equal to the number of processor cores)

plot

Description

Plot a chart based on the found points.

Possible types

bool

Default value

False

Type of return value

tuple of two arrays (thresholds, fnr)

Usage examples

from catboost import CatBoostClassifier, Pool
from catboost.utils import get_roc_curve, get_fnr_curve

train_data = [[1,3],
              [0,4],
              [1,7],
              [3,0]]
train_labels = [1,0,1,1]
catboost_pool = Pool(train_data, train_labels)
model = CatBoostClassifier(learning_rate=0.03)
model.fit(train_data, train_labels, verbose=False)
roc_curve_values = get_roc_curve(model, catboost_pool)
(thresholds, fnr) = get_fnr_curve(curve=roc_curve_values, plot=True)
print(thresholds)
print(fnr)

Output:

[1.         0.54411915 0.50344403 0.        ]
[1.         0.33333333 0.         0.        ]