def confusion_matrix Found at: sklearn.metrics._classification

@_deprecate_positional_args
def confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None,??normalize=None):
? ? """Compute confusion matrix to evaluate the accuracy of a classification.
? ??
? ? By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}` is equal to the number of observations known to be in group :math:`i` and predicted to be in group :math:`j`.
? ??
? ? Thus in binary classification, the count of true negatives is
? ? :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
? ? :math:`C_{1,1}` and false positives is :math:`C_{0,1}`.
? ??
? ? Read more in the :ref:`User Guide <confusion_matrix>`.
? ??
? ? Parameters
? ? ----------
? ? y_true : array-like of shape (n_samples,) Ground truth (correct) target values.
? ? y_pred : array-like of shape (n_samples,) Estimated targets as returned by a classifier.
? ? labels : array-like of shape (n_classes), default=None.??List of labels to index the matrix. This may be used to reorder
? ? or select a subset of labels.??If ``None`` is given, those that appear at least once in ``y_true`` or ``y_pred`` are used in sorted order.
? ??
? ? sample_weight : array-like of shape (n_samples,), default=None. Sample weights.
? ??
? ? .. versionadded:: 0.18
? ??
? ? normalize : {'true', 'pred', 'all'}, default=None. Normalizes confusion matrix over the true (rows), predicted (columns)
? ? conditions or all the population. If None, confusion matrix will not be normalized.
? ??
? ? Returns
? ? -------
? ? C : ndarray of shape (n_classes, n_classes)
? ? Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and prediced label being j-th class.
? ??
? ? References
? ? ----------
? ? .. [1] `Wikipedia entry for the Confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix>`_??(Wikipedia and other references may use a different convention for axes)

在:sklear. metrics._classification找到的def confusion_matrix

@_deprecate_positional_args
defconfusion_matrix (y_true, y_pred， *， label =None, sample_weight=None， normalize= None):
計算混淆矩陣來評估分類的準確性。

根據定義，一個混淆矩陣:math: ' C '是這樣的:math: ' C_{i, j} '等于已知在:math: ' i '組和預測在:math: ' j '組的觀測數。

因此，在二元分類法中，true negatives的數量是
? ? :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
? ? :math:`C_{1,1}` and false positives is :math:`C_{0,1}`.

更多信息見:ref: ' User Guide <confusion_matrix> '。</confusion_matrix>

參數
----------
y_true:類數組形狀(n_samples，) Ground truth (correct)目標值。
y_pred:分類器返回的估計目標的類數組形狀(n_samples，)。
標簽:類數組形狀(n_classes)，默認=無。索引矩陣的標簽列表。這可以用于重新排序
或者選擇標簽的子集。如果給出了' ' None ' '，則在' ' y_true ' '或' ' y_pred ' '中至少出現一次的值將按排序順序使用。

sample_weight:類似數組的形狀(n_samples，)，默認=None。樣本權重。

. .versionadded:: 0.18

{'true'， 'pred'， 'all'}， default=None。對真實(行)、預測(列)的混淆矩陣進行規范化
條件或所有的人口。如果沒有，混淆矩陣將不會被標準化。

返回
-------
C:形狀的ndarray (n_classes, n_classes)
第i行和第j列項表示真標簽樣本個數為第i類，謂詞標簽樣本個數為第j類的混淆矩陣。
&nbsp; &nbsp;&nbsp;
引用
----------
. .[1] '用于混淆矩陣的維基百科條目<https: en.wikipedia.org="" wiki="" confusion_matrix=""> ' _(維基百科和其他引用可能對軸使用不同的約定)</https:>