ROC Report#

Imports#

import warnings

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks.performance import RocReport


def custom_formatwarning(msg, *args, **kwargs):
    return str(msg) + '\n'

warnings.formatwarning = custom_formatwarning

Generating data#

iris = load_iris(as_frame=True)
clf = LogisticRegression(penalty='none')
frame = iris.frame
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=55)
clf.fit(X_train, y_train)
ds = Dataset(pd.concat([X_test, y_test], axis=1),
            features=iris.feature_names,
            label='target')

Running roc_report Check#

check = RocReport()
check.run(ds, clf)

ROC Report

Calculate the ROC curve for each class.

Additional Outputs
The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined as sensitivity + specificity - 1


Total running time of the script: ( 0 minutes 0.035 seconds)

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