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.
#First do our imports import yellowbrick 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, label_encoder=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 : the Scikit-Learn estimator
Should be an instance of a classifier or __init__ will return an error.
ax : the matplotlib axis to plot the figure on (if None, a new axis will be created)
classes : list, default: None
a list of class names to use in the confusion_matrix. This is passed to the ‘labels’ parameter of 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, values that appear at least once in y_true or y_pred are 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.
>>> 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()
Renders the classification report Should only be called internally, as it uses values calculated in Score and score calls this method.
Whether the heatmap should represent “% of True” or raw counts
score(X, y, sample_weight=None, percent=True)¶
Generates the Scikit-Learn confusion_matrix and applies this to the appropriate axis
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
sample_weight: optional, passed to the confusion_matrix
percent: optional, Boolean. Determines whether or not the confusion_matrix
should be displayed as raw numbers or as a percent of the true predictions. Note, if using a subset of classes in __init__, percent should be set to False or inaccurate percents will be displayed.