Visualizing machine learning data
When visualizing machine learning data, several important aspects should be considered to ensure effective communication and interpretation of the insights. The following should be considered (these are not tool-specific):
- Understand the data: Gain a deep understanding of the data you are working with. Analyze its structure, distributions, and relationships to identify key variables and patterns. This understanding will guide you in selecting appropriate visualization techniques.
- Choose the right visualization techniques: Select visualization techniques that best represent the characteristics and relationships within the data. Common techniques include scatter plots, line charts, bar charts, histograms, heatmaps, and network graphs. Choose techniques that effectively convey the information you want to communicate. In the next chapter, we will discover the different visualizations that Qlik offers.
- Simplify and reduce complexity: Keep...