Frequently, machine learning problems in the real world suffer from the curse of dimensionality; you have fewer training instances than you'd like and the predictive signal is distributed (often unpredictably!) across many different features.
Sometimes when the your target variable is continuously-valued, there simply aren't enough instances to predict these values to the precision of regression. In this case, we can sometimes transform the regression problem into a classification problem by binning the continuous values into makeshift classes.
To help the user select the optimal number of bins, the BalancedBinningReference visualizer takes the target variable y as input and generates a histogram with vertical lines indicating the recommended value points to ensure that the data is evenly distributed into each bin.
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| Quick Method |
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| Workflow |
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from yellowbrick.datasets import load_concrete from yellowbrick.target import BalancedBinningReference
# Load the concrete dataset X, y = load_concrete()
# Instantiate the visualizer visualizer = BalancedBinningReference()
visualizer.fit(y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure
The same functionality above can be achieved with the associated quick method balanced_binning_reference. This method will build the BalancedBinningReference object with the associated arguments, fit it, then (optionally) immediately show it.
from yellowbrick.datasets import load_concrete from yellowbrick.target import balanced_binning_reference
# Load the dataset X, y = load_concrete()
# Use the quick method and immediately show the figure balanced_binning_reference(y)
To learn more, please read Rebecca Bilbro's article "Creating Categorical Variables from Continuous Data."
yellowbrick.target.binning