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.