When developing machine learning models, data preparation takes up about 80% of the time, according to Andrew Ng. This step is crucial because the quality of your data directly affects your model's accuracy. One key aspect of data quality is the accuracy of the labels in your dataset.
Label errors can have a big impact. Studies have shown that error rates in real-world datasets can be as high as 50%, and even a 20% error rate can make a dataset unusable for training models. Fixing bad data is expensive, so it's important to use the best tools and methods to ensure your data is accurate from the start. In this article, we show how label errors affect model performance using five different datasets. Our results indicate that an error percentage higher than 20% can drastically decrease a model's accuracy. To address this issue, we employ label error detection techniques to automatically enhance the quality of our datasets.
This post will show how errors in datasets can impact machine learning models in NLP tasks for text classification evaluated using a test set. We generated datasets with different error ratios, then randomly selected samples and changed their labels to incorrect ones to test the model's robustness against label errors.
This exploratory study was conducted using five datasets:
We created two versions of each dataset: one with 1,000 samples and another with 10,000 samples. We then introduced errors into the datasets by randomly changing labels to incorrect ones, varying the error rates from 10% to 100% at intervals of 10%.
Here are the findings based on the results of our experiments in Case Study 1 and Case Study 2.
We conducted experiments to see how label error detection could reduce the time required to improve dataset quality. We used four datasets, each containing a 10% error rate.
Based on our empirical study, relying on label error detection (LED) and auto-correction without reviewer intervention can improve dataset quality by up to 8.7%, as this feature corrects label errors up to 87% in the DBpedia_14 dataset. However, it is important not to rely solely on auto-correction, as human review is always preferable. Label error detection enhances the human labeling experience by providing reviewers with small subsets of data with a high probability of errors, thus reducing the time spent on the review process by up to 95%.
To ensure high-quality datasets, we offer several features:
High-quality data is essential for accurate and reliable machine learning models. By using advanced tools and methods to prevent and correct label errors, you can significantly improve your data quality and model performance.
Discover how our intelligent labeling features can help you achieve high-quality data for your AI projects. Contact us at sales@datasaur.ai to book a demo and see how our solutions can streamline your data processes and elevate your projects.
