混淆矩阵

使用混淆矩阵来评估虹膜数据集中分类器输出质量的示例。对角线元素表示预测标签等于真实标签的点的数量，而非对角线元素是那些被分类器错误标记的点。混淆矩阵的对角线值越高越好，表明许多正确的预测。

这些图显示了按类支持大小（每个类中的元素数量）进行规范化和不进行规范化的混乱矩阵。在类不平衡的情况下，这种规范化可能很有趣，可以更直观地解释哪个类被错误分类。

这里的结果并没有达到应有的水平，因为我们对正规化参数C的选择不是最好的。在现实生活中的应用中，通常使用以下方式选择此参数 调整估计器的超参数 .

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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()

相关实例

识别手写数字

Recognizing hand-written digits

Iris数据集的主成分分析（PCA）

Principal Component Analysis (PCA) on Iris Dataset

最近邻分类

Nearest Neighbors Classification

在虹膜数据集中绘制不同的支持者分类器

Plot different SVM classifiers in the iris dataset