Role of Annotations in Model Performance

5. Role of Annotations in Model Performance#

Data annotation is the process of labeling data to make it usable for training machine learning models.

5.1. Annotations are the foundation of model performance.#

  • They provide the ground truth that a model learns from.

  • Machine learning depends heavily on data quality

  • High-quality, well labeled annotations = Better model generalization & accuracy.

5.2. Machine Learning = 80% Data Management + 20% Model Building#

“In machine learning, 80% of your time is spent preparing and cleaning the data, and only 20% of the time is spent on actual model building.”
— Andrew Ng, former Stanford AI Director/Professor

  • Annotations fall into the 80% of the effort spent on data prep and cleaning.

5.3. TLDR: Garbage in, garbage out#

  • Poor or inconsistent annotations can lead to poor performance, while high-quality annotations can significantly improve accuracy.

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