使用PCA、Isomap对乳腺癌数据集进行降维，使用KNN对数据集进行分类，并显示所有结果

好的，我可以为您展示如何使用PCA、Isomap进行降维，并使用KNN对数据集进行分类，并显示所有结果。

首先，我们加载乳腺癌数据集，并对数据进行预处理：

from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.manifold import Isomap
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import numpy as np

data = load_breast_cancer()
X, y = data.data, data.target
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

接下来，我们使用PCA和Isomap对数据进行降维，并使用KNN进行分类。

使用PCA进行降维和分类：

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_pca = np.mean(y_pred == y_test)
print("PCA + KNN accuracy: {:.2f}%".format(accuracy_pca * 100))

使用Isomap进行降维和分类：

isomap = Isomap(n_components=2)
X_isomap = isomap.fit_transform(X_scaled)
X_train, X_test, y_train, y_test = train_test_split(X_isomap, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_isomap = np.mean(y_pred == y_test)
print("Isomap + KNN accuracy: {:.2f}%".format(accuracy_isomap * 100))

最后，我们将所有结果进行显示：

print("PCA + KNN accuracy: {:.2f}%".format(accuracy_pca * 100))
print("Isomap + KNN accuracy: {:.2f}%".format(accuracy_isomap * 100))

完整代码如下：

from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.manifold import Isomap
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import numpy as np

data = load_breast_cancer()
X, y = data.data, data.target
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_pca = np.mean(y_pred == y_test)
print("PCA + KNN accuracy: {:.2f}%".format(accuracy_pca * 100))

isomap = Isomap(n_components=2)
X_isomap = isomap.fit_transform(X_scaled)
X_train, X_test, y_train, y_test = train_test_split(X_isomap, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_isomap = np.mean(y_pred == y_test)
print("Isomap + KNN accuracy: {:.2f}%".format(accuracy_isomap * 100))

print("PCA + KNN accuracy: {:.2f}%".format(accuracy_pca * 100))
print("Isomap + KNN accuracy: {:.2f}%".format(accuracy_isomap * 100))