cleanlab automatically finds and fixes errors in your ML datasets.

This reduces manual work needed to fix data issues and helps train reliable ML models on partially mislabeled datasets. cleanlab has already found thousands of label errors in ImageNet, MNIST, and other popular ML benchmarking datasets, so let’s get started with yours!


1. Install cleanlab.#

pip install cleanlab

2. Find label errors with get_noise_indices.#

cleanlab’s get_noise_indices function tells you which examples in your dataset are likely mislabeled. At a minimum, it expects two inputs - your data’s given labels, y, and predicted probabilities, pyx, from some trained model (Note: these must be out-of-sample predictions where the data points were held out from the model during training, which can be obtained via cross-validation).

Setting sorted_index_method instructs cleanlab to return the indices of potential mislabeled examples, ordered by the likelihood of label error estimate via prob_given_label scores (predicted probability of given label according to the model).

from cleanlab.pruning import get_noise_indices

ordered_label_errors = get_noise_indices(


The predicted probabilities, pyx, from your model must be out-of-sample! You should never provide predictions on the same data points used to train the model - this would reflect predictions of an overfitted model, making it unsuitable for finding label errors. To compute the out-of-sample predicted probabilities of the entire dataset, you can use cross-validation.

3. Train robust models with noisy labels using LearningWithNoisyLabels.#

cleanlab’s LearningWithNoisyLabels adapts any classification model, clf, to a more reliable one by allowing it to train directly on partially mislabeled datasets.

When the .fit() method is called, it automatically identifies and removes any examples that are deemed “noisy” in the provided dataset before returning a final trained model.

from sklearn.linear_model import LogisticRegression
from cleanlab.classification import LearningWithNoisyLabels

clf = LogisticRegression() # Here we've used sklearn's Logistic Regression model, but this can be any classifier that implements sklearn's API.
lnl = LearningWithNoisyLabels(clf=clf), s=y)