报错`load_boston` has been removed from scikit-learn since version 1.2.

这个报错是因为从 Scikit-learn 1.2 开始，`load_boston` 已被移除。它是一个加载波士顿房价数据集的函数。如果你需要使用波士顿房价数据集，你可以使用以下代码：

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

这个代码会从 OpenML 数据库中获取波士顿房价数据集，并将其作为一个 Bunch 对象返回。你可以使用`boston.data`来获取数据集的特征，使用`boston.target`来获取目标变量（即房价）。

相关问题

`load_boston` has been removed from scikit-learn since version 1.2.

I apologize for the confusion. You are correct, the `load_boston` function has been removed from scikit-learn since version 1.2. Instead, you can use the `fetch_openml` function to download the Boston dataset from the OpenML repository. Here's an example:

from sklearn.datasets import fetch_openml

# Load the Boston dataset
boston = fetch_openml(data_id=531)

# Get the feature matrix and target vector
X = boston.data
y = boston.target

# Print the shape of the feature matrix and the first 5 rows
print(X.shape)
print(X[:5, :])

# Print the shape of the target vector and the first 5 elements
print(y.shape)
print(y[:5])

In this example, we use the `fetch_openml` function to download the Boston dataset. Then, we extract the feature matrix `X` and the target vector `y` from the downloaded dataset.

Please make sure you have scikit-learn version 1.2 or later installed in your Python environment to use this method. If you encounter any issues or have further questions, feel free to ask.

load_boston has been removed from scikit-learn since version 1.2.

您提到的是对的，`load_boston`函数确实不再直接存在于scikit-learn的最新版本中，比如从1.2及以后的版本。这个变化的原因可能是为了简化库的结构和减少依赖，或者是为了鼓励用户直接访问更广泛的数据源，如`pandas`库中的数据。

如果你想继续使用波士顿房价数据集，可以采取以下几种方式：

1. **通过pandas加载**：可以从UCI Machine Learning Repository或其他数据存储库下载CSV文件，然后使用`pandas`读取：

import pandas as pd
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
boston_df = pd.read_csv(url, header=None, names=column_names)

2. **使用fetch_california_housing**：虽然不是原生的波士顿数据集，但它是相似的加利福尼亚州住房数据，可以在scikit-learn中找到：

from sklearn.datasets import fetch_california_housing
california_housing = fetch_california_housing()

3. **利用其他的机器学习库**：像`seaborn`或`statsmodels`库也可能提供类似数据集。

请注意，无论采用哪种方法，你都需要自行处理数据预处理工作，包括编码分类变量、填充缺失值（如果有的话）、标准化等。