Data-Centric Machine Learning with Python by Jonas Christensen

Author:Jonas Christensen
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2024-02-22T00:00:00+00:00

You will notice that these slicing functions operate on specific subsets of the data and enable us to incorporate domain knowledge into the labeling process. Therefore, designing effective slicing functions requires a combination of domain knowledge, feature engineering, and experimentation. Here are some key considerations for designing and implementing slicing functions:

Identify relevant slices: Determine the specific subsets or slices of the data that are relevant to the labeling task. This involves understanding the problem domain, analyzing the data, and identifying distinct patterns or characteristics.

Define slicing conditions: Specify the conditions or rules that capture the desired subsets of the data. These conditions can be based on feature thresholds, pattern matching, statistical properties, or any other relevant criteria.

Evaluate and iterate: Assess the performance of the slicing functions by comparing the assigned labels to ground truth labels or existing labeling sources. Iterate on the design of the slicing functions, refining the conditions and rules to improve the quality of the assigned labels.

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