ConfusionMatrix visualizer is a ScoreVisualizer that takes a
fitted scikit-learn classifier and a set of test X and y values and
returns a report showing how each of the test values predicted classes
compare to their actual classes. Data scientists use confusion matrices
to understand which classes are most easily confused. These provide
similar information as what is available in a ClassificationReport, but
rather than top-level scores, they provide deeper insight into the
classification of individual data points.
Below are a few examples of using the ConfusionMatrix visualizer; more information can be found by looking at the scikit-learn documentation on confusion matrices.
from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from yellowbrick.classifier import ConfusionMatrix
# We'll use the handwritten digits data set from scikit-learn. # Each feature of this dataset is an 8x8 pixel image of a handwritten number. # Digits.data converts these 64 pixels into a single array of features digits = load_digits() X = digits.data y = digits.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.2, random_state=11) model = LogisticRegression() # The ConfusionMatrix visualizer taxes a model cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9]) # Fit fits the passed model. This is unnecessary if you pass the visualizer a pre-fitted model cm.fit(X_train, y_train) # To create the ConfusionMatrix, we need some test data. Score runs predict() on the data # and then creates the confusion_matrix from scikit-learn. cm.score(X_test, y_test) # How did we do? cm.poof()
Visual confusion matrix for classifier scoring.
ConfusionMatrix(model, ax=None, classes=None, sample_weight=None, percent=False, label_encoder=None, cmap='YlOrRd', fontsize=None, **kwargs)¶
Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). A confusion matrix shows each combination of the true and predicted classes for a test data set.
The default color map uses a yellow/orange/red color scale. The user can choose between displaying values as the percent of true (cell value divided by sum of row) or as direct counts. If percent of true mode is selected, 100% accurate predictions are highlighted in green.
Requires a classification model.
- model : estimator
Must be a classifier, otherwise raises YellowbrickTypeError
- ax : matplotlib Axes, default: None
The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required).
- sample_weight: array-like of shape = [n_samples], optional
confusion_matrixto weight the samples.
- percent: bool, default: False
Determines whether or not the confusion_matrix is displayed as counts or as a percent of true predictions. Note, if specifying a subset of classes, percent should be set to False or inaccurate figures will be displayed.
- classes : list, default: None
a list of class names to use in the confusion_matrix. This is passed to the
sklearn.metrics.confusion_matrix(), and follows the behaviour indicated by that function. It may be used to reorder or select a subset of labels. If None, classes that appear at least once in
y_predare used in sorted order.
- label_encoder : dict or LabelEncoder, default: None
When specifying the
classesargument, the input to
score()must match the expected labels. If the
ydatasets have been encoded prior to training and the labels must be preserved for the visualization, use this argument to provide a mapping from the encoded class to the correct label. Because typically a Scikit-Learn
LabelEncoderis used to perform this operation, you may provide it directly to the class to utilize its fitted encoding.
- cmap : string, default:
Specify a colormap to define the heatmap of the predicted class against the actual class in the confusion matrix.
- fontsize : int, default: None
Specify the fontsize of the text in the grid and labels to make the matrix a bit easier to read. Uses rcParams font size by default.
>>> from yellowbrick.classifier import ConfusionMatrix >>> from sklearn.linear_model import LogisticRegression >>> viz = ConfusionMatrix(LogisticRegression()) >>> viz.fit(X_train, y_train) >>> viz.score(X_test, y_test) >>> viz.poof()
- confusion_matrix_ : array, shape = [n_classes, n_classes]
The numeric scores of the confusion matrix
- class_counts_ : array, shape = [n_classes,]
The total number of each class supporting the confusion matrix
Renders the classification report; must be called after score.
Finalize executes any subclass-specific axes finalization steps.
- kwargs: dict
generic keyword arguments.
The user calls poof and poof calls finalize. Developers should implement visualizer-specific finalization methods like setting titles or axes labels, etc.
score(X, y, **kwargs)¶
Draws a confusion matrix based on the test data supplied by comparing predictions on instances X with the true values specified by the target vector y.
- X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
- y : ndarray or Series of length n
An array or series of target or class values