# Label Error Detection
## Introduction
Datasaur's **Label error detection** feature revolutionizes the way you approach data labeling by automating the identification of inaccuracies in your dataset. It flags potential label errors and suggests alternatives, significantly improving data integrity and model performance.
## **Key Features**
Manual data review is notoriously time-consuming and prone to human error. Leveraging automated error detection:
1. **Boosts efficiency:** Seamlessly pinpoint label errors without sifting through your entire dataset.
2. **Enhances focus:** Apply a specific error threshold to zero in on the most questionable labels, optimizing your review process.
3. **Improves accuracy:** Receive intelligent label suggestions, enabling quick, precise corrections.
## Terminologies
1. **Label errors**: Discrepancies or inaccuracies in the assigned labels of your dataset, which can mislead your model's learning process.
2. **Error possibility**: A probability score that indicates the likelihood of a label being incorrect, allowing for prioritized review.
3. **Label correction suggestions:** Offering alternative labels suggested by the model for accurate and refined dataset curation.
## **Quick Start Guide**
Unlock the full potential of label error detection in your row-based project with four simple steps:
### **Step 1: Enable the Feature**
Activate the **Label error detection** extension from the [Manage extensions](https://docs.datasaur.ai/advanced/extensions) button (gear icon) in the extension panel on the right.
Activating label error detection.
### **Step 2: Configure Settings**
In the **Label error detection** extensions (on the right side), configure:
1. The **Target column** for input text.
2. The **Target question** for labels.
Setting up your detection parameters.
### **Step 3: Run Detection**
Ensure all rows are labeled before clicking **Find label errors**. This might take a moment.
Initiating error detection.
Detected errors in rows will appear in the **Label errors** section. Adjust the error possibility threshold to fine-tune your review focus.
Identifying errors in your dataset.
### **Step 4: Review and Adjust**
Click the problematic rows from the **Label errors** section and make necessary corrections by editing labels directly or accepting suggested changes.
Focusing on label errors for correction.
## **Troubleshooting Common Issues**
* **Feature not working:** Ensure all rows are labeled, and the extension is properly enabled.
* **Incorrect suggestions:** Adjust the **Error possibility** threshold for more accurate detection.
Happy labeling!
