How do you improve your machine learning visualizations?

1 Choose the right plot

The first step to improve your machine learning visualizations is to choose the right type of plot for your data and purpose. Different plots have different strengths and limitations, and you should consider factors such as the number and dimensionality of your variables, the distribution and scale of your values, the relationships and patterns you want to highlight, and the audience and context of your presentation. For example, scatter plots are good for showing correlations and outliers, bar charts are good for comparing categorical or discrete data, line charts are good for showing trends and changes over time, and heat maps are good for showing density and intensity.

2 Use colors and labels wisely

The second step to improve your machine learning visualizations is to use colors and labels wisely. Colors and labels can help you emphasize, contrast, group, or differentiate your data points, as well as convey meaning and emotion. However, you should also avoid using too many colors or labels, as they can make your visualizations cluttered, confusing, or misleading. You should also consider the accessibility and readability of your colors and labels, and use consistent and appropriate color schemes, legends, titles, axes, and annotations.

3 Add interactivity and animation

The third step to improve your machine learning visualizations is to add interactivity and animation. Interactivity and animation can help you create more engaging and dynamic visualizations, as they can allow you to explore different perspectives, scenarios, or hypotheses, as well as reveal more details, insights, or stories. For example, you can use sliders, buttons, dropdowns, or tooltips to control or display different variables, filters, or metrics, or you can use transitions, movements, or sounds to show changes, progressions, or comparisons. However, you should also balance the trade-off between interactivity and animation and simplicity and clarity, and avoid adding unnecessary or distracting elements.

4 Avoid common pitfalls

The fourth step to improve your machine learning visualizations is to avoid common pitfalls that can compromise the quality and credibility of your visualizations. Some of these pitfalls include using inappropriate or misleading scales, axes, or ranges, omitting or manipulating data or information, violating the principles of data integrity or ethics, or ignoring the feedback or expectations of your audience or stakeholders. You should always check and verify your data sources, methods, and assumptions, as well as test and evaluate your visualizations for accuracy, validity, and usability.

5 Use tools and libraries

The fifth step to improve your machine learning visualizations is to use tools and libraries that can help you create and customize your visualizations more easily and efficiently. There are many tools and libraries available for different languages, platforms, and purposes, such as matplotlib, seaborn, plotly, bokeh, dash, or streamlit for Python, ggplot2, shiny, or leaflet for R, or d3.js, chart.js, or highcharts for JavaScript. You should explore and compare the features, functionalities, and documentation of these tools and libraries, and choose the ones that suit your needs and preferences.

6 Learn from examples

The sixth and final step to improve your machine learning visualizations is to learn from examples of good and bad visualizations, as well as from the feedback and suggestions of others. You can find many examples of machine learning visualizations online, such as on blogs, websites, books, or courses, or you can create your own portfolio or gallery of visualizations. You can also ask for feedback or advice from your peers, mentors, or experts, or join online communities or forums where you can share and discuss your visualizations. By learning from examples, you can improve your skills, knowledge, and creativity in machine learning visualizations.

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?