What are some tips for handling conflicting data in machine learning?

1 Define the problem

Before you start cleaning and processing the data, you need to define the problem and the goal of your machine learning project. This will help you to identify the relevant data sources, features, and labels, as well as the expected outputs and performance metrics. You also need to understand the domain and context of the data, such as the data collection methods, the data types, the data formats, and the data quality standards. This will help you to assess the potential sources and types of conflicting data, and to prioritize the most critical and impactful ones.

2 Explore the data

Once you have defined the problem and the data sources, you need to explore the data and perform some descriptive and exploratory analysis. This will help you to get a sense of the data distribution, variability, and patterns, as well as to identify any anomalies, outliers, or errors. You can use various tools and techniques for exploring the data, such as summary statistics, histograms, box plots, scatter plots, correlation matrices, or heat maps. You can also use some data profiling or data quality tools to check the completeness, validity, accuracy, consistency, and uniqueness of the data.

3 Resolve the conflicts

After exploring the data and detecting any conflicts, you need to resolve them according to the problem and data quality criteria. Different methods and approaches are used for resolving conflicting data, depending on the nature and severity of the conflicts. These include deleting or ignoring the conflicting data, which is a simple but potentially data-loss inducing method; imputing or replacing the conflicting data with reasonable estimates; merging or consolidating the conflicting data from different sources; and flagging or labeling the conflicting data with indicators or tags. Each approach has its pros and cons, such as preserving data integrity versus introducing uncertainty, reducing redundancy versus losing information granularity, and highlighting data quality issues versus informing machine learning models.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?