In this lecture, we will explore how visualizations can be used beyond data exploration tasks, to understand something as complex as Machine Learning decision making.
A machine learnign model derives the relationship between input and output using the computational units of its architecture.
Consider the below example, where an image is input into Resnet50, a popular image classification model and our task is to understand what led to the given decision.
The above image was classified as Broom (0.59% probability), Honeycomb (0.54% probability), Plunger (0.52% probability), Tennis Ball (0.51% probability) or Panpipe (0.46% probability).
Visualizationthe decisions: Heatmap is one popular technique to explore the important features.