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被分类器错误标记的点数。混淆矩阵的对角线值越高越好,表明预测正确率高。
这些图显示了混淆矩阵,分别按类别支持大小(每个类别的元素数量)进行归一化和未进行归一化。这种归一化在类别不平衡的情况下可能很有趣,以便更直观地了解哪个类别被错误分类。
这里的结果并不理想,因为我们选择的正则化参数 C 不是最佳选择。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. ]]
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 0.198 秒)
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