Visualizer
~yellowbrick.model_selection.dropping_curve.DroppingCurveQuick Method
~yellowbrick.model_selection.dropping_curve.dropping_curveModels Classification, Regression, Clustering
Workflow Model Selection
A feature dropping curve (FDC) shows the relationship between the score and the number of features used. This visualizer randomly drops input features, showing how the estimator benefits from additional features of the same type. For example, how many air quality sensors are needed across a city to accurately predict city-wide pollution levels?
Feature dropping curves helpfully complement rfecv (RFECV). In the air quality sensor example, RFECV finds which sensors to keep in the specific city. Feature dropping curves estimate how many sensors a similar-sized city might need to track pollution levels.
Feature dropping curves are common in the field of neural decoding, where they are called neuron dropping curves (example, panels C and H). Neural decoding research often quantifies how performance scales with neuron (or electrode) count. Because neurons do not correspond directly between participants, we use random neuron subsets to simulate what performance to expect when recording from other participants.
To show how this works in practice, consider an image classification example using handwritten digits.
from sklearn.svm import SVC from sklearn.datasets import load_digits
from yellowbrick.model_selection import DroppingCurve
# Load dataset X, y = load_digits(return_X_y=True)
# Initialize visualizer with estimator visualizer = DroppingCurve(SVC())
# Fit the data to the visualizer visualizer.fit(X, y) # Finalize and render the figure visualizer.show()
This figure shows an input feature dropping curve. Since the features are informative, the accuracy increases with more larger feature subsets. The shaded area represents the variability of cross-validation, one standard deviation above and below the mean accuracy score drawn by the curve.
The visualization can be interpreted as the performance if we knew some image pixels were corrupted. As an alternative interpretation, the dropping curve roughly estimates the accuracy if the image resolution was downsampled.
The same functionality can be achieved with the associated quick method dropping_curve. This method will build the DroppingCurve with the associated arguments, fit it, then (optionally) immediately show the visualization.
from sklearn.svm import SVC from sklearn.datasets import load_digits
from yellowbrick.model_selection import dropping_curve
# Load dataset X, y = load_digits(return_X_y=True)
dropping_curve(SVC(), X, y)
yellowbrick.model_selection.dropping_curve