DecisionBoundaries Vizualizer

The DecisionBoundariesVisualizer is a bivariate data visualization algorithm that plots the decision boundaries of each class.

from sklearn.model_selection import train_test_split as tts
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons
from sklearn.neighbors import KNeighborsClassifier
from yellowbrick.contrib.classifier import DecisionViz

data_set = make_moons(noise=0.3, random_state=0)

X, y = data_set
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = tts(X, y, test_size=.4, random_state=42)

viz = DecisionViz(
    KNeighborsClassifier(3), title="Nearest Neighbors",
    features=['Feature One', 'Feature Two'], classes=['A', 'B']
)
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.show()

(Source code, png, pdf)

from sklearn.svm import SVC
from sklearn.model_selection import train_test_split as tts
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons
from sklearn.neighbors import KNeighborsClassifier
from yellowbrick.contrib.classifier import DecisionViz

data_set = make_moons(noise=0.3, random_state=0)

X, y = data_set
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = tts(X, y, test_size=.4, random_state=42)


viz = DecisionViz(
    SVC(kernel="linear", C=0.025), title="Linear SVM",
    features=['Feature One', 'Feature Two'], classes=['A', 'B']
)
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.show()

(Source code, png, pdf)

API Reference

class yellowbrick.contrib.classifier.boundaries.DecisionBoundariesVisualizer(estimator, ax=None, x=None, y=None, features=None, classes=None, show_scatter=True, step_size=0.0025, markers=None, pcolormesh_alpha=0.8, scatter_alpha=1.0, encoder=None, is_fitted='auto', force_model=False, **kwargs)

Bases: ClassificationScoreVisualizer

DecisionBoundariesVisualizer is a bivariate data visualization algorithm that plots the decision boundaries of each class.

Parameters

estimatorestimator

A scikit-learn estimator that should be a classifier. If the model is not a classifier, an exception is raised. If the internal model is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by is_fitted.

axmatplotlib Axes, default: None

The axes to plot the figure on. If not specified the current axes will be used (or generated if required).

xstring, default: None

The feature name that corresponds to a column name or index postion in the matrix that will be plotted against the x-axis

ystring, default: None

The feature name that corresponds to a column name or index postion in the matrix that will be plotted against the y-axis

featureslist of strings, default: None

The names of the features or columns

classeslist of str, defult: None

The class labels to use for the legend ordered by the index of the sorted classes discovered in the fit() method. Specifying classes in this manner is used to change the class names to a more specific format or to label encoded integer classes. Some visualizers may also use this field to filter the visualization for specific classes. For more advanced usage specify an encoder rather than class labels.

show_scatterboolean, default: True

If boolean is True, then a scatter plot with points will be drawn on top of the decision boundary graph

step_sizefloat percentage, default: 0.0025

Determines the step size for creating the numpy meshgrid that will later become the foundation of the decision boundary graph. The default value of 0.0025 means that the step size for constructing the meshgrid will be 0.25%% of differenes of the max and min of x and y for each feature.

markersiterable of strings, default: ,od*vh+

Matplotlib style markers for points on the scatter plot points

pcolormesh_alphafloat, default: 0.8

Sets the alpha transparency for the meshgrid of model boundaries

scatter_alphafloat, default: 1.0

Sets the alpha transparency for the scatter plot points

encoderdict or LabelEncoder, default: None

A mapping of classes to human readable labels. Often there is a mismatch between desired class labels and those contained in the target variable passed to fit() or score(). The encoder disambiguates this mismatch ensuring that classes are labeled correctly in the visualization.

is_fittedbool or str, default=”auto”

Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be modified. If “auto” (default), a helper method will check if the estimator is fitted before fitting it again.

force_modelbool, default: False

Do not check to ensure that the underlying estimator is a classifier. This will prevent an exception when the visualizer is initialized but may result in unexpected or unintended behavior.

kwargsdict

Keyword arguments passed to the visualizer base classes.

draw(X, y=None, **kwargs)

Called from the fit method, this method creates a decision boundary plot, and if self.scatter is True, it will scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set.

finalize(**kwargs)

Sets the title and axis labels and adds a legend.

Parameters

kwargs: generic keyword arguments.

Notes

Generally this method is called from show and not directly by the user.

fit(X, y=None, **kwargs)

The fit method is the primary drawing input for the decision boundaries visualization since it has both the X and y data required for the viz and the transform method does not.

Parameters

Xndarray or DataFrame of shape n x m

A matrix of n instances with m features

yndarray or Series of length n

An array or series of target or class values

kwargsdict

Pass generic arguments to the drawing method

Returns

selfinstance

Returns the instance of the visualizer

fit_draw(X, y=None, **kwargs)

Fits a transformer to X and y then returns visualization of features or fitted model.

fit_draw_show(X, y=None, **kwargs)

Fits a transformer to X and y then returns visualization of features or fitted model. Then calls show to finalize.