fetch_california_housing (original) (raw)
sklearn.datasets.fetch_california_housing(*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False, n_retries=3, delay=1.0)[source]#
Load the California housing dataset (regression).
Read more in the User Guide.
Parameters:
data_homestr or path-like, default=None
Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
download_if_missingbool, default=True
If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site.
return_X_ybool, default=False
If True, returns (data.data, data.target) instead of a Bunch object.
Added in version 0.20.
as_framebool, default=False
If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns.
Added in version 0.23.
n_retriesint, default=3
Number of retries when HTTP errors are encountered.
Added in version 1.5.
delayfloat, default=1.0
Number of seconds between retries.
Added in version 1.5.
Returns:
datasetBunch
Dictionary-like object, with the following attributes.
datandarray, shape (20640, 8)
Each row corresponding to the 8 feature values in order. If as_frame is True, data is a pandas object.
targetnumpy array of shape (20640,)
Each value corresponds to the average house value in units of 100,000. If as_frame is True, target is a pandas object.
feature_nameslist of length 8
Array of ordered feature names used in the dataset.
DESCRstr
Description of the California housing dataset.
framepandas DataFrame
Only present when as_frame=True. DataFrame with data andtarget.
Added in version 0.23.
**(data, target)**tuple if return_X_y is True
A tuple of two ndarray. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second ndarray of shape (n_samples,) containing the target samples.
Added in version 0.20.
Notes
This dataset consists of 20,640 samples and 9 features.
Examples
from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() print(housing.data.shape, housing.target.shape) (20640, 8) (20640,) print(housing.feature_names[0:6]) ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']