contingency_matrix#

sklearn.metrics.cluster.contingency_matrix(labels_true, labels_pred, *, eps=None, sparse=False, dtype=<class 'numpy.int64'>)[source]#

Build a contingency matrix describing the relationship between labels.

Parameters:

labels_truearray-like of shape (n_samples,): Ground truth class labels to be used as a reference.
labels_predarray-like of shape (n_samples,): Cluster labels to evaluate.
epsfloat, default=None: If a float, that value is added to all values in the contingency matrix. This helps to stop NaN propagation. If None, nothing is adjusted.
sparsebool, default=False: If True, return a sparse CSR continency matrix. If eps is not None and sparse is True will raise ValueError.

Added in version 0.18.
dtypenumeric type, default=np.int64: Output dtype. Ignored if eps is not None.

Added in version 0.24.

Returns:

contingency{array-like, sparse}, shape=[n_classes_true, n_classes_pred]: Matrix \(C\) such that \(C_{i, j}\) is the number of samples in true class \(i\) and in predicted class \(j\). If eps is None, the dtype of this array will be integer unless set otherwise with the dtype argument. If eps is given, the dtype will be float. Will be a sklearn.sparse.csr_matrix if sparse=True.

Examples

>>> from sklearn.metrics.cluster import contingency_matrix
>>> labels_true = [0, 0, 1, 1, 2, 2]
>>> labels_pred = [1, 0, 2, 1, 0, 2]
>>> contingency_matrix(labels_true, labels_pred)
array([[1, 1, 0],
       [0, 1, 1],
       [1, 0, 1]])