Parallel coordinates displays each feature as a vertical axis spaced evenly along the horizontal, and each instance as a line drawn between each individual axis. This allows many dimensions; in fact given infinite horizontal space (e.g. a scrollbar), an infinite number of dimensions can be displayed!
Data scientists use this method to detect clusters of instances that have similar classes, and to note features that have high variance or different distributions.
# Load the classification data set
data = load_data('occupancy')
# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features].as_matrix()
y = data.occupancy.as_matrix()# Instantiate the visualizer
visualizer = ParallelCoordinates(classes=classes, features=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.poof() # Draw/show/poof the data
Parallel coordinates can take a long time to draw since each instance is represented by a line for each feature. Worse, this time is not well spent since a lot of overlap in the visualization makes the parallel coordinates less understandable. To fix this, pass the sample keyword argument to the visualizer with a percentage to randomly sample from the dataset.
Additionally the domain of each feature may make the visualization hard to interpret. In the above visualization, the domain of the light feature is from in [0, 1600], far larger than the range of temperature in [50, 96]. A normalization methodology can be applied to change the range of features to [0,1]. Try using minmax, minabs, standard, l1, or l2 normalization to change perspectives in the parallel coordinates:
# Instantiate the visualizer
visualizer = ParallelCoordinates(
classes=classes, features=features,
normalize='standard', sample=0.1,
)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.poof() # Draw/show/poof the data
yellowbrick.features.pcoords