What is Data Visualization in a Machine Learning Context?
Data visualization algorithms create images from raw data and display hidden correlations so that humans can process the information more effectively. Data visualization is also an important evaluation metric for deep learning, since the ultimate goal of artificial intelligence is to create a machine that can understand and respond to data even better than a human could.
Machine Learning Data Visualization Examples:
When designing and evaluating a new algorithm, one of the first steps is exploratory data analysis (EDA). The point is to find the most efficient learning approach for a given problem. For the human researcher to understand what’s working and what’s not, the model’s results are often displayed graphically. Since these datasets cover many variables and are so “high dimensional,” several new data visualization techniques have been developed specifically for deep learning systems. Some of the most common new tools for interpreting high-dimensional relationships are:
- Parallel coordinate plots
- Scatterplot matrices
- Scagnostics
- Multidimensional scaling
- T-sne algorithm
