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@@ -172,9 +172,9 @@ Many methods and their default parameters are not covered here. Check out the [d
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## Use any custom model's predicted probabilities to find label errors in 1 line of code
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pred_probs (num_examples x num_classes matrix of predicted probabilities) should already be computed on your own, with any classifier. For best results, pred_probs should be obtained in a holdout/out-of-sample manner (e.g. via cross-validation).
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* cleanlab can do this for you via [`cleanlab.count.estimate_cv_predicted_probabilities`](https://docs.cleanlab.ai/stable/cleanlab/count.html)]
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* Tutorial with more info: [[here](https://docs.cleanlab.ai/stable/tutorials/pred_probs_cross_val.html)]
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* Examples how to compute pred_probs with: [[CNN image classifier (PyTorch)](https://docs.cleanlab.ai/stable/tutorials/image.html)]
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* cleanlab can do this for you via [`cleanlab.count.estimate_cv_predicted_probabilities`](https://docs.cleanlab.ai/stable/cleanlab/count.html)
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* Tutorial with more info: [here](https://docs.cleanlab.ai/stable/tutorials/pred_probs_cross_val.html)
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* Examples how to compute pred_probs with: [CNN image classifier (PyTorch)](https://docs.cleanlab.ai/stable/tutorials/image.html)
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```python
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# label issues are ordered by likelihood of being an error. First index is most likely error.

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