Navigating Challenges of Data Quality in Machine Learning

Machine learning (ML) models require large datasets to train, but focusing on quantity over quality can have severely negative effects on a model’s performance and accuracy. Businesses across nearly every sector rely on ML applications to assist with critical decisions like who to hire, how to allocate monthly budgets, or determining who’s at risk of developing cancer, all of which place the utmost importance on data quality. This blog discusses some of the risks and challenges associated with data quality in machine learning, followed by highlights of the best practices and techniques for overcoming these pitfalls.

The importance of data quality in machine learning

Data quality has a direct impact on the effectiveness of machine learning models. Using poor-quality data to train models can lead to biased and inaccurate results (a.k.a. inferences) and ultimately to sub-optimal decisions. Some of the most substantial risks of using poor data quality for machine learning include:

• Reduced model accuracy, precision, and recall
• Biased model predictions
• Model hallucinations
• Data leaks and breaches of sensitive information

These risks can have grave real-world consequences. For example, companies using ML algorithms to help filter out certain candidates in the hiring process could face legal action if the model shows bias against protected groups like women or minorities.

Data quality challenges

So, what exactly is high-quality data, and what causes quality issues? Data can be described as high-quality when it’s complete, accurate, relevant, unbiased, and free of harmful content such as PII, bias, or toxicity. For data scientists and machine learning developers, the most serious data quality issues include the following:

Sparse data

Incomplete or missing information in training data can lead to inaccurate predictions. This issue may stem from bad data sources that lack key information to begin with, but it can also result from overly aggressive data cleansing methods with high false positive rates that further reduce the available data unnecessarily.

Noisy data

Irrelevant, duplicate, and inaccurate information in training data can negatively affect the ML model’s performance. Often, this issue is caused by a lack of cleansing and analysis before data is used for training.

Harmful data

There are a few ways that the data ingested by ML models can be harmful. It could be biased for or against particular groups of people, resulting in a machine learning application that makes untrustworthy decisions. If it contains private information about individuals or businesses, the model may have accidental leaks or malicious actors might intentionally expose them. It could even be manipulated by hackers in a way that purposely damages the ML model or affects its decision-making process, which is also known as training data poisoning.

Best practices for improving machine learning data quality

Companies can choose from numerous techniques to overcome data quality challenges and ensure peak performance and accuracy from their machine learning applications. We list five primary data quality strategies and their various tactics in the sections below.

Data cleansing

Data cleansing involves several techniques for cleaning up a dataset before it’s sed to train and develop an ML model.

These methods include:

• Mean/median/mode imputation - Filling in missing information using statistical averages.
• PII data masking - Removing personally identifiable information and replacing it with something else.
• Synthetic data generation - Replacing sensitive or unwanted information with realistic-looking data.
• Deduplication - Removing duplicate entries in datasets to prevent them from introducing bias or inaccuracies

Automated quality validation

Using automated tools to continuously validate data quality will ensure better model training, performance, and accuracy. Some of the techniques used by these tools include:

These methods include:

• Schema validation - Comparing incoming data to a pre-selected schema to ensure it meets expected standards.
• Statistical validation - Monitoring for sudden statistical changes in data distributions to prevent poisoning.
• Completeness checks - Identifying missing datapoints or incomplete records so they can be addressed before affecting the model.
• Anomaly detection - Scanning incoming data in real-time to detect anomalous patterns or outliers.

Exploratory data analysis (EDA)

Exploratory data analysis involves thoroughly analyzing and visualizing data before ingestion to help identify quality issues like bias or incompleteness. Some common EDA techniques include:

• Univariate analysis - Examining individual variables through visualizations like histograms and box plots.
• Bivariate analysis - Analyzing relationships between pairs of variables with visualizations like scatter plots and correlation matrices.
• Multivariate analysis- Visualizing multidimensional data with tools like principal component analysis (PCA).

Active learning

Active learning is a machine learning approach that involves having the ML model query interactively for new information rather than passively ingesting whatever the data scientist feeds it. Active learning enables the model to select the data it needs for training from very large datasets, allowing teams to prioritize labeling that information and eliminating the need for data scientists to label all the rest. Active learning techniques include:

• Uncertainty sampling - Labeling the information that the ML model is most uncertain about.
• Diversity sampling - Sampling data that are as diverse as possible to label information that is representative of the entire dataset.
• Query by committee - Using multiple models to select information and then labeling the data they disagree about.

Model updates

Data patterns may change over time, and more high-quality training data may become available after a model goes into production. Regularly updating models helps prevent data quality from degrading and continuously improves performance and accuracy. Model adaptation techniques include:

• Online learning - Allowing models to continuously learn from new, fresh data in real-time.
• Ensemble learning - Aggregating learning from two or more models to improve inference accuracy.
  
• Retraining- Periodically taking models offline to retrain on fresh data, either at pre-set intervals or as triggered by performance monitoring tools.

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