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最近质心分类#

最近质心分类的示例用法。它将绘制每个类别的决策边界。

None 0.8133333333333334
0.2 0.82

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap

from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.neighbors import NearestCentroid

# 导入一些数据来玩玩
iris = datasets.load_iris()
# 我们只取前两个特征。我们可以通过使用一个二维数据集来避免这种丑陋的切片操作。
X = iris.data[:, :2]
y = iris.target

# 创建色彩映射
cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"])
cmap_bold = ListedColormap(["darkorange", "c", "darkblue"])

for shrinkage in [None, 0.2]:
    # 我们创建了一个最近质心分类器的实例并拟合了数据。
    clf = NearestCentroid(shrink_threshold=shrinkage)
    clf.fit(X, y)
    y_pred = clf.predict(X)
    print(shrinkage, np.mean(y == y_pred))

    _, ax = plt.subplots()
    DecisionBoundaryDisplay.from_estimator(
        clf, X, cmap=cmap_light, ax=ax, response_method="predict"
    )

    # 还要绘制训练点
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20)
    plt.title("3-Class classification (shrink_threshold=%r)" % shrinkage)
    plt.axis("tight")

plt.show()