Improving Data Quality: The Foundation for Accurate and Reliable Models

This article delves into the significance of feeding high-quality data into machine learning models and sheds light on several data quality issues that, if left unaddressed, can undermine the integrity of data science projects.

Rahul S


In the realm of machine learning, data quality is of paramount importance. The phrase “garbage in, garbage out” succinctly captures the idea that the output of a machine learning model is only as good as the quality of the data it is fed.

Algorithms rely on the assumption that the data they receive adheres to certain standards and exhibits desirable properties. However, the reality is that our world, ourselves, and the data we generate are far from perfect, carrying inherent imperfections. Understanding and mitigating these imperfections is crucial for building robust and reliable machine learning models.

Let’s delve into them one by one.


It is important to differentiate between data and quality data. While the term “big data” has gained prominence in recent years, it does not automatically equate to quality data. Merely having a large volume of data does not guarantee its quality or usefulness for training machine learning models. Quality data encompasses various aspects, including accuracy, completeness, consistency, and relevance. By prioritizing data quality, ML engineers can ensure that their models are built on a solid foundation, leading to more accurate and reliable results.

Machine learning algorithms traditionally operate under several assumptions regarding training data, each of which can be a potential source of data quality issues. These assumptions include:

  1. Equal representation of existing classes: ML algorithms assume that the training data contains an equal representation of all the classes or categories that the model needs to learn. However, in real-world scenarios, some classes may be underrepresented or have limited examples, leading to biased models that perform poorly on these minority classes.