What are the best practices for using data visualization to diagnose ML model errors?

1 Error metrics

Before you can visualize your ML model errors, you need to define and measure them. Error metrics are numerical values that quantify how well your model fits the data and meets your objectives. Depending on your problem domain and model type, you may use different error metrics, such as accuracy, precision, recall, F1-score, mean squared error, mean absolute error, etc. You should choose error metrics that are relevant, interpretable, and aligned with your goals.

2 Error distribution

One of the simplest ways to visualize your ML model errors is to plot their distribution. This can help you understand the overall magnitude, range, and variance of your errors. For example, you can use a histogram, a box plot, or a density plot to show how your errors are distributed across your data points. You can also compare the error distribution of your training and validation sets to check for overfitting or underfitting.

3 Error correlation

Another way to visualize your ML model errors is to explore their correlation with other variables or features. This can help you discover how your errors are influenced by different factors and identify potential sources of bias or noise. For example, you can use a scatter plot, a heat map, or a correlation matrix to show how your errors are related to your input features, output labels, or model predictions. You can also use a facet grid or a grouped bar chart to show how your errors vary by different categories or groups.

4 Error types

A more advanced way to visualize your ML model errors is to classify them into different types based on their causes and characteristics. This can help you understand the nature and complexity of your errors and prioritize the most critical or frequent ones. For example, you can use a confusion matrix, a precision-recall curve, or a ROC curve to show the types of errors in your classification models, such as false positives, false negatives, true positives, or true negatives. You can also use a residual plot, a Q-Q plot, or a leverage plot to show the types of errors in your regression models, such as outliers, heteroscedasticity, non-linearity, or high leverage.

5 Error patterns

The most sophisticated way to visualize your ML model errors is to detect and analyze their patterns over time or space. This can help you understand the dynamics and trends of your errors and identify potential anomalies or changes. For example, you can use a time series plot, a line chart, or a moving average to show how your errors fluctuate over time. You can also use a map, a spatial plot, or a cluster analysis to show how your errors are distributed over space.

6 Error visualization tools

To create effective and interactive visualizations for your ML model errors, you need to use appropriate tools and libraries. Depending on your programming language and environment, you may use different tools and libraries, such as Matplotlib, Seaborn, Plotly, Bokeh, Altair, etc. for Python; ggplot2, Shiny, Plotly, Leaflet, etc. for R; or Tableau, Power BI, Qlik, etc. for GUI-based tools. You should choose tools and libraries that are easy to use, flexible, and compatible with your data and model formats.

7 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?