Improving Training Data with Outlier Detection

Encord Active offers a robust solution to identify and label outliers for pre-defined metrics, custom metrics, and label classes using precomputed interquartile ranges. Outlier detection is crucial as it can distort statistical analysis and affect the performance of machine learning models. Encord Active empowers users to detect and address problematic data points early in their machine learning pipeline, leading to improved data quality and more reliable machine learning models. The platform offers tools for data cleaning, balancing data distribution, iterating on the dataset based on model performance and feedback, and making data-driven decisions through visualization and dataset filtering. By leveraging these features, users can continually optimize their training data to achieve optimal model performance and ensure high-quality datasets are used for training machine learning models.

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