Label Drift#

This notebooks provides an overview for using and understanding the NLP label drift check.


What Is Label Drift?#

Drift is simply a change in the distribution of data over time, and it is also one of the top reasons why machine learning model’s performance degrades over time.

Label drift is when drift occurs in the label itself.

For more information on drift, please visit our drift guide.

How Deepchecks Detects Label Drift#

This check detects label drift by using univariate measures on the label.

from deepchecks.nlp.datasets.classification import tweet_emotion
from deepchecks.nlp.checks import LabelDrift

Load Data#

For this example, we’ll use the tweet emotion dataset, which is a dataset of tweets labeled by one of four emotions: happiness, anger, sadness and optimism.

Let’s see how our data looks like:

text label user_age gender days_on_platform user_region
0 No but that's so cute. Atsu was probably shy a... happiness 24.97 Male 2729 Middle East/Africa
1 Rooneys fucking untouchable isn't he? Been fuc... anger 21.66 Male 1376 Asia Pacific
2 Tiller and breezy should do a collab album. Ra... happiness 37.29 Female 3853 Americas
3 @user broadband is shocking regretting signing... anger 15.39 Female 1831 Europe
4 @user Look at those teef! #growl anger 54.37 Female 4619 Europe

Run Check#

As there’s natural drift in this dataset, we can expect to see some drift in the “optimism” label:

check = LabelDrift()
result =, test_dataset=test_ds)
Label Drift

Total running time of the script: (0 minutes 0.123 seconds)

Gallery generated by Sphinx-Gallery