> ## Documentation Index > Fetch the complete documentation index at: https://docs.encord.com/llms.txt > Use this file to discover all available pages before exploring further. # Shortcuts and Prediction Types Overview shortcuts for Data, Labels, and Predictions and Prediction Types for Predictions, give you a quick method to filter your unwanted or problematic images. They are shortcuts to improving datasets and model performance. They give you a quick launch pad to improve your data and your model performance. The overview shortcuts for Data, Labels, and Predictions in the *Overview* tab are generalized. Contact us if you want personalized shortcuts populating the *Overview* tab. ## Data Issue Shortcuts ![Data Shortcuts](https://storage.googleapis.com/docs-media.encord.com/static/img/active/active-shortcuts-data.gif) **Data Issues Overview** | Title | Metric Type | Ontology Type | | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ------------- | | **Duplicates** - Duplicate and near-duplicate images. Images with a **Uniqueness** score of 0.0 to 0.00001 are flagged as duplicates. | `image` | | | **Blur** - Images that are too blurry. Images with a **Sharpness** score of 0.0 to 0.005 are flagged as blurry. | `image` | | | **Dark** - Images that are too dark. Images with a **Brightness** score of 0.0 to 0.1 are flagged as too dark. | `image` | | | **Bright** - Images that are too bright. Images with a **Brightness** score of 0.7 to 1.0 are flagged as too bright. | `image` | | ## Label Issue Shortcuts ![Label Shortcuts](https://storage.googleapis.com/docs-media.encord.com/static/img/active/active-shortcuts-labels.gif) **Label Issues Overview** | Title | Metric Type | Ontology Type | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------ | | **Aspect Ratio** - Identifies objects with a low aspect ratio value. Images with an **Aspect Ratio** score of 0.0 to 0.1 are flagged as having an issue. | `image` | `bounding box`, `polygon`, `rotatable bounding box` | | **Border Proximity** - Identifies annotations that are too close to image borders. Images with a **Border Proximity** score of 1 are flagged as being too close to the border. | `image` | `bounding box`, `point`, `polygon`, `polyline`, `rotatable bounding box`, `skeleton` | | **Low Annotation Quality** - Compares image classifications against the 60 most similar images. Images with a **Classification Quality** score of 0.0 to 0.02 are flagged as having an issue. | `image` | `radio` | | **Relative Area** - Identifies annotations that are relatively too small compared to the size of the image. Images with a **Relative Area** score of 0.0 to 0.003 are flagged being too small. | `image` | `bounding box`, `polygon`, `rotatable bounding box` | ## Prediction Issues and Types ![Predictions Shortcuts](https://storage.googleapis.com/docs-media.encord.com/static/img/active/active-shortcuts-predictions.gif) **Prediction Issues Overview** | Title | Metric Type | Ontology Type | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------- | ------------------------------------------------------------------------------------ | | **Shape Outliers** - Duplicate and near-duplicate images. Images with a **Polygon Shape Anomaly** score of 0.0 to 0.02 are flagged as duplicates. | `image` | | | **Border Proximity** - Identifies annotations that are too close to image borders. Images with a **Border Proximity** score of 1 are flagged as being too close to the border. | `image` | `bounding box`, `point`, `polygon`, `polyline`, `rotatable bounding box`, `skeleton` | | **Aspect Ratio** - Identifies objects with a low aspect ratio value. Images with an **Aspect Ratio** score of 0.0 to 0.1 are flagged as having an issue. | `image` | `bounding box`, `polygon`, `rotatable bounding box` | **Prediction Types** | Title | Metric Type | Ontology Type | | -------------------------------------------------------------------------------------------------------------- | ----------- | ------------- | | **All** - All model outcomes. | `image` | | | **All Positives** - All model outcomes that are True Positive and False Positive. | `image` | | | **True Positives** - All model outcomes where the model correctly identified objects. | `image` | | | **False Positives** - All model outcomes where the model incorrectly identified objects as the correct object. | `image` | | | **False Negatives** - All model outcomes where the model incorrectly identified objects as the wrong object. | `image` | | ## Use Issue and Prediction Type shortcuts This process assumes that there is already one or more Projects in Active. **To use Issue and Prediction Type shortcuts:** 1. Log in to the Encord platform. The landing page for the Encord platform appears. 2. Click **Active** in the main menu. The landing page for Active appears. 3. Click the Project. The landing page for the Project appears with the *Explorer* tab selected. 4. Click **Data**, **Labels**, or **Predictions**. The Explorer workspace changes based on what you clicked. The Overview tab displays with the shortcuts. 5. Click a shortcut. A filter is applied to the images. The images appearing in the Explorer workspace changes depending on which shortcut you click. 6. Click **Filter** if you want to modify the filter settings. 7. Further search, sort, and filter the data. 8. Create a Collection based on the results. 9. Create a Dataset (and Project) and send that Dataset to Annotate. 10. Further annotate your data. 11. Rinse and repeat until you have the dataset you need for optimal model performance.