How much data is required to get an accurate model from Piccolo AI?

Determining the exact amount of data required to achieve a desired level of accuracy in a machine learning model is challenging, as it depends on the specific use case and the variability of influencing factors. Each application has unique contributing factors that affect model outcomes and the degree of data variance across those factors.

The role of the domain expert is crucial. They should:

  • Identify all potential influencing factors.
  • Rank these factors by their expected impact on the model’s performance.
  • Decide which factors can be controlled or eliminated outside of the model.
  • Develop a reasonable testing methodology based on these considerations.

We recommend an iterative approach:

  1. Start with a Small Dataset: Begin with a modest amount of data to build an initial model. This helps gain insights into which factors contribute most to model errors.
  2. Analyze and Adjust: Use the initial model to understand errors and identify influential factors.
  3. Expand Data Collection: Based on insights gained, collect additional data focusing on the most impactful factors. Conduct this in stages, alternating between data collection, analysis, and model refinement.

Initial model development can sometimes be performed with a small number of samples—perhaps 50–100 per class—to gain preliminary insights. However, the specific amount can vary widely depending on the complexity of the problem and the desired accuracy. Generally, more data leads to better-performing and more reliable models.

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