• API Reference - RocReport

ROC Report

This notebook provides an overview for using and understanding the ROC Report check.

Structure:

• What is the ROC Report check?

• Generate data & model

• Run the check

• Define a condition

What is the ROC Report check?

The ROCReport check calculates the ROC curve for each class. The ROC curve is a plot of TPR (true positive rate) with respect to FPR (false positive rate) at various thresholds (ROC curve).

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 import RocReport


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

warnings.formatwarning = custom_formatwarning

Generate data & model

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')

Run the Check

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

Define a condition

A condition for minimum allowed AUC score per class can be defined. Here, we define minimum AUC score to be 0.7.

check = RocReport()
check.add_condition_auc_greater_than(0.7).run(ds, clf)

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Train Test Performance

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Regression Error Distribution