Why Machine Learning Requires High Quality Data

?Machine learning (ML) models depend on high quality data to generate accurate predictive insights and make informed decisions. The reliability of these predictions is contingent on large volumes of data that are correct and error-free. Low quality data, which may be misaligned or biased, compromises the model's ability to make accurate predictions, akin to teaching a child with incorrect information. Here’s how to ensure data quality when training an ML model, evaluated through six key dimensions: completeness, uniqueness, timeliness, validity, accuracy, and consistency.

1.???? Completeness: Ensuring all necessary information is present. Incomplete data prevents the model from learning essential patterns. For example, missing transaction dates in a customer transaction dataset can impair model training.

2.???? Uniqueness: Data should be free from duplicates. Duplicate records can confuse the model, hindering its ability to identify accurate patterns. For instance, if a dataset for dog breed identification contains too many photos of Labradors, the model may struggle to recognise other breeds.

3.???? Timeliness: Data must be current and reflective of the phenomenon being modelled. Outdated data can lead to irrelevant predictions. For example, using months-old stock prices to predict current market fluctuations is ineffective.

4.???? Validity: Data should conform to predefined standards and definitions, including type and format. For example, a date format inconsistency, such as 08-12-2019 instead of the standard year-month-date format, affects data validity.

5.???? Accuracy: Data must be correct and truthful. Incorrect labels, such as mislabelling images of cats as dogs, compromise the data's accuracy. Accuracy differs from validity, which focuses on type and format, while accuracy is about the correctness of content.

6.???? Consistency: Data should be uniform without contradictions. Inconsistent data, where the same entity appears differently (e.g., John Smith vs. J. Smith), can mislead the model and degrade prediction accuracy.

Since data is the only perspective through which an ML model views the world, it is crucial to provide it with complete and correct data. Many data quality issues can be resolved by purposefully collecting more high quality data, ensuring the ML models are trained effectively for accurate and reliable predictions.