from sklearn.datasets import fetch_openml boston = fetch_openml() 怎么解决

非常抱歉，我之前的回答仍然有误。如果您使用 `fetch_openml()` 函数加载波士顿房价数据集时出现了错误，可能是因为您没有提供数据集的名称或数据集 ID。您可以使用以下代码加载波士顿房价数据集：

from sklearn.datasets import fetch_openml
boston = fetch_openml(name='boston')

请注意，这里的 `name='boston'` 参数指定了您要加载的数据集的名称。这将返回一个字典对象，您可以通过以下方式访问数据和目标：

X = boston.data
y = boston.target

请注意，这个函数返回的数据类型是 Pandas 的 DataFrame 类型。如果您希望将其转换为 NumPy 数组，可以使用以下代码：

import numpy as np
X = np.array(X)
y = np.array(y)

相关问题

怎么解决无法从sklearn.datasets中导入fetch_mldata。

`fetch_mldata`已经被弃用，所以无法从`sklearn.datasets`中导入。您可以尝试以下替代方案：

1. 使用`fetch_openml`代替`fetch_mldata`，例如：

   from sklearn.datasets import fetch_openml
   mnist = fetch_openml('mnist_784')

2. 从外部下载数据集并手动加载数据集，例如：

   import numpy as np
   import gzip
   
   def load_mnist(path, kind='train'):
       """Load MNIST data from `path`"""
       labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)
       images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)
   
       with gzip.open(labels_path, 'rb') as lbpath:
           labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)
   
       with gzip.open(images_path, 'rb') as imgpath:
           images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)
   
       return images, labels
   
   X_train, y_train = load_mnist('path/to/mnist', kind='train')
   X_test, y_test = load_mnist('path/to/mnist', kind='t10k')

补全代码，使用 PCA 对MNIST的数据降维到2维 做一下KMeans并显示： import numpy as np from sklearn.datasets import fetch_openml # Load data from https://www.openml.org/d/554 X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) print(X.shape,y.shape) #(70000, 784) (70000,) print(np.unique(y)) ##可只选择前10000条数据做实验 X=X[:10000] y=y[:10000]

import numpy as np 
from sklearn.datasets import fetch_openml 
from sklearn.decomposition import PCA 
from sklearn.cluster import KMeans 
import matplotlib.pyplot as plt 

# Load data from https://www.openml.org/d/554 
X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) 

# Print the shape of X and y
print(X.shape, y.shape) #(70000, 784) (70000,)

# Print the unique values of y
print(np.unique(y))

# Select the first 10000 samples for experiment
X = X[:10000]
y = y[:10000]

# Perform PCA to reduce dimensionality to 2
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# Perform KMeans clustering on the reduced data
kmeans = KMeans(n_clusters=10)
kmeans.fit(X_pca)

# Plot the clustered data
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=kmeans.labels_, cmap='viridis')
plt.title('KMeans Clustering on MNIST Data')
plt.show()