How to navigate issues in your machine learning image data
Data cleaning is an important yet often underestimated step in real-world machine learning projects. The presence of bad data, including mislabeled examples, outliers, and duplicates, can have a significant impact on the performance and reliability of machine learning models. To address these issues, data cleaning tools like Cleanlab have emerged as valuable helpers for automatically detecting data issues. Cleanlab utilizes confident learning techniques, implementing a model-in-the-loop approach.
While data cleaning tools aid in identifying potential data issues, a thorough review and exploration of the results are still necessary to gain a comprehensive understanding of the underlying problems.
Advanced visualization tools play an indispensable role in this context. Tools like Renumics Spotlight don’t just provide cutting-edge visualizations to identify failure patterns; they also empower us to thoroughly review data issues. An example of their capability is the Similarity Map, an advanced, highly interactive dimensionality reduction plot that leverages model embeddings for the identification of problematic clusters.
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