混淆矩阵#

混淆矩阵使用示例，用于评估分类器在鸢尾花数据集上的输出质量。对角线元素表示预测标签等于真实标签的点数，而非对角线元素表示分类器错误标记的那些点。混淆矩阵的对角线值越高越好，表示许多预测正确。

这些图显示了有和没有按类支持大小（每个类中的元素数量）归一化的混淆矩阵。如果存在类不平衡，这种归一化对于更直观地了解哪些类被错误分类可能很有趣。

这里的结果并不理想，因为我们选择的正则化参数C不是最佳的。在实际应用中，此参数通常使用调整估计器的超参数来选择。

Confusion matrix, without normalization
[[13  0  0]
 [ 0 10  6]
 [ 0  0  9]]
Normalized confusion matrix
[[1.   0.   0.  ]
 [0.   0.62 0.38]
 [0.   0.   1.  ]]

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import datasets, svm
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel="linear", C=0.01).fit(X_train, y_train)

np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
titles_options = [
    ("Confusion matrix, without normalization", None),
    ("Normalized confusion matrix", "true"),
]
for title, normalize in titles_options:
    disp = ConfusionMatrixDisplay.from_estimator(
        classifier,
        X_test,
        y_test,
        display_labels=class_names,
        cmap=plt.cm.Blues,
        normalize=normalize,
    )
    disp.ax_.set_title(title)

    print(title)
    print(disp.confusion_matrix)

plt.show()

脚本总运行时间：（0分钟0.196秒）