sklearn.metrics.cluster.contingency_matrix(labels_true, labels_pred, eps=None, sparse=False)[source]

Build a contingency matrix describing the relationship between labels.

labels_true : int array, shape = [n_samples]

Ground truth class labels to be used as a reference

labels_pred : array, shape = [n_samples]

Cluster labels to evaluate

eps : None or float, optional.

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.

sparse : boolean, optional.

If True, return a sparse CSR continency matrix. If eps is not None, and sparse is True, will throw ValueError.

New in version 0.18.

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. If eps is given, the dtype will be float. Will be a scipy.sparse.csr_matrix if sparse=True.