sklearn.metrics.cluster.contingency_matrix
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sklearn.metrics.cluster.contingency_matrix(labels_true, labels_pred, eps=None, sparse=False)
[source]
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Build a contingency matrix describing the relationship between labels.
Parameters: |
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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. |
Returns: |
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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 . |