sklearn.metrics.cluster
.contingency_matrix¶
- sklearn.metrics.cluster.contingency_matrix(labels_true, labels_pred, *, eps=None, sparse=False, dtype=<class 'numpy.int64'>)¶
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. Ifeps
is notNone
andsparse
isTrue
will raise ValueError.New in version 0.18.
- dtypenumeric type, default=np.int64
Output dtype. Ignored if
eps
is notNone
.New 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 thedtype
argument. Ifeps
is given, the dtype will be float. Will be asklearn.sparse.csr_matrix
ifsparse=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]])