• API Reference - ConflictingLabels

Conflicting Labels

This notebook provides an overview for using and understanding the conflicting labels check.

Structure:

• What are Conflicting Labels?

• Load Data

• Run the Check

• Define a Condition

What are Conflicting Labels?

The check searches for identical samples with different labels. This can occur due to either mislabeled data, or when the data collected is missing features necessary to separate the labels. If the data is mislabled, it can confuse the model and can result in lower performance of the model.

import pandas as pd

from deepchecks.tabular import Dataset

from deepchecks.tabular.checks import ConflictingLabels
from deepchecks.tabular.datasets.classification.phishing import load_data

Load Data

phishing_dataframe = load_data(as_train_test=False, data_format='Dataframe')
phishing_dataset = Dataset(phishing_dataframe, label='target', features=['urlLength', 'numDigits', 'numParams', 'num_%20', 'num_@', 'bodyLength', 'numTitles', 'numImages', 'numLinks', 'specialChars'])

Run the Check

ConflictingLabels().run(phishing_dataset)

We can also check label ambiguity on a subset of the features:

ConflictingLabels(n_to_show=1).run(phishing_dataset)

ConflictingLabels(columns=['urlLength', 'numDigits']).run(phishing_dataset)

Define a Condition

Now, we define a condition that enforces that the ratio of samples with conflicting labels should be 0. A condition is deepchecks’ way to validate model and data quality, and let you know if anything goes wrong.

check = ConflictingLabels()
check.add_condition_ratio_of_conflicting_labels_less_or_equal(0)
result = check.run(phishing_dataset)
result.show(show_additional_outputs=False)

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Identifier Label Correlation

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Data Duplicates