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ML：分类预测问题中评价指标(ER/混淆矩阵P-R-F1/ROC-AUC/RP/mAP)简介、使用方法、代码实现、案例应用之详细攻略
CNN之性能指标：卷积神经网络中常用的性能指标(IOU/AP/mAP、混淆矩阵)简介、使用方法之详细攻略
sklearn.metrics中常用的函数参数
confusion_matrix函数解释
返回值：混淆矩阵，其第i行和第j列条目表示真实标签为第i类、预测标签为第j类的样本数。
| 预测 | |||
| 0 | 1 | ||
| 真实 | 0 | ||
| 1 | |||
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): 计算混淆矩阵来评估分类的准确性。 根据定义，一个混淆矩阵:math: ' C '是这样的:math: ' C_{i, j} '等于已知在:math: ' i '组和预测在:math: ' j '组的观测数。 因此，在二元分类法中，true negatives的数量是 :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}`. 更多信息见:ref: ' User Guide <confusion_matrix> '。</confusion_matrix> 参数 ---------- y_true:类数组形状(n_samples，) Ground truth (correct)目标值。 y_pred:分类器返回的估计目标的类数组形状(n_samples，)。 标签:类数组形状(n_classes)，默认=无。索引矩阵的标签列表。这可以用于重新排序 或者选择标签的子集。如果给出了' ' None ' '，则在' ' y_true ' '或' ' y_pred ' '中至少出现一次的值将按排序顺序使用。 sample_weight:类似数组的形状(n_samples，)，默认=None。样本权重。 . .versionadded:: 0.18 {'true'， 'pred'， 'all'}， default=None。对真实(行)、预测(列)的混淆矩阵进行规范化 条件或所有的人口。如果没有，混淆矩阵将不会被标准化。 返回 ------- C:形状的ndarray (n_classes, n_classes) 第i行和第j列项表示真标签样本个数为第i类，谓词标签样本个数为第j类的混淆矩阵。
引用 ---------- . .[1] '用于混淆矩阵的维基百科条目<https: en.wikipedia.org="" wiki="" confusion_matrix=""> ' _(维基百科和其他引用可能对轴使用不同的约定)</https:> |
| Examples -------- >>> from sklearn.metrics import confusion_matrix >>> y_true = [2, 0, 2, 2, 0, 1] >>> y_pred = [0, 0, 2, 2, 0, 2] >>> confusion_matrix(y_true, y_pred) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) In the binary case, we can extract true positives, etc as follows: >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() >>> (tn, fp, fn, tp) (0, 2, 1, 1) |
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| """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type not in ("binary", "multiclass"): raise ValueError("%s is not supported" % y_type) if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) n_labels = labels.size if n_labels == 0: raise ValueError("'labels' should contains at least one label.") elif y_true.size == 0: return np.zeros((n_labels, n_labels), dtype=np.int) elif np.all([l not in y_true for l in labels]): raise ValueError("At least one label specified must be in y_true") if sample_weight is None: sample_weight = np.ones(y_true.shape[0], dtype=np.int64) else: sample_weight = np.asarray(sample_weight) check_consistent_length(y_true, y_pred, sample_weight) if normalize not in ['true', 'pred', 'all', None]: raise ValueError("normalize must be one of {'true', 'pred', " "'all', None}") n_labels = labels.size label_to_ind = {y:x for x, y in enumerate(labels)} # convert yt, yp into index y_pred = np.array([label_to_ind.get(x, n_labels + 1) for x in y_pred]) y_true = np.array([label_to_ind.get(x, n_labels + 1) for x in y_true]) # intersect y_pred, y_true with labels, eliminate items not in labels ind = np.logical_and(y_pred < n_labels, y_true < n_labels) y_pred = y_pred[ind] y_true = y_true[ind] # also eliminate weights of eliminated items sample_weight = sample_weight[ind] # Choose the accumulator dtype to always have high precision if sample_weight.dtype.kind in {'i', 'u', 'b'}: dtype = np.int64 else: dtype = np.float64 cm = coo_matrix((sample_weight, (y_true, y_pred)), shape=(n_labels, n_labels), dtype=dtype).toarray() with np.errstate(all='ignore'): if normalize == 'true': cm = cm / cm.sum(axis=1, keepdims=True) elif normalize == 'pred': cm = cm / cm.sum(axis=0, keepdims=True) elif normalize == 'all': cm = cm / cm.sum() cm = np.nan_to_num(cm) return cm |
