hello,
On Sep 28 2022 I was working with the Boston Housing data and the exercises in module 02 supervised-learning. We received a message that there was an ethical problem with the Boston Housing data and that scikit-learn was recommending a switch to the California Housing data, for which they provided links.
I ended up modifying the mglearn/datasets.py file, adding the import line and a function load_extended_california(). This allows the rest of the code in the notebook to function as written with the California housing data.
from sklearn.datasets import fetch_california_housing
def load_extended_california():
housing = fetch_california_housing()
X = housing.data
X = MinMaxScaler().fit_transform(housing.data)
X = PolynomialFeatures(degree=2, include_bias=False).fit_transform(X)
return X, housing.target
hello,
On Sep 28 2022 I was working with the Boston Housing data and the exercises in module 02 supervised-learning. We received a message that there was an ethical problem with the Boston Housing data and that scikit-learn was recommending a switch to the California Housing data, for which they provided links.
I ended up modifying the mglearn/datasets.py file, adding the import line and a function load_extended_california(). This allows the rest of the code in the notebook to function as written with the California housing data.
from sklearn.datasets import fetch_california_housing
def load_extended_california():
housing = fetch_california_housing()
X = housing.data