- fairlearn.datasets.fetch_boston(*, cache=True, data_home=None, as_frame=True, return_X_y=False, warn=True)#
Load the boston housing dataset (regression).
Download it if necessary.
real 5. - 50.
The Boston house-price data of D. Harrison, and D.L. Rubinfeld .
Referenced in Belsley, Kuh & Welsch .
This dataset has known fairness issues . There’s a “lower status of population” (LSTAT) parameter that you need to look out for and a column that is a derived from the proportion of people with a black skin color that live in a neighborhood (B) . See the references at the bottom for more detailed information.
Here’s a table of all the variables in order:
per capita crime rate by town
proportion of residential land zoned for lots over 25,000 sq.ft.
proportion of non-retail business acres per town
Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
nitric oxides concentration (parts per 10 million)
average number of rooms per dwelling
proportion of owner-occupied units built prior to 1940
weighted distances to five Boston employment centres
index of accessibility to radial highways
full-value property-tax rate per $10,000
pupil-teacher ratio by town
1000(Bk - 0.63)^2 where Bk is the proportion of Black people by town
% lower status of the population
Median value of owner-occupied homes in $1000’s
Read more in the User Guide.
New in version 0.5.0.
cache (bool, default=True) – Whether to cache downloaded datasets using joblib.
data_home (str, default=None) – Specify another download and cache folder for the datasets. By default, all fairlearn data is stored in ‘~/.fairlearn-data’ subfolders.
as_frame (bool, default=True) –
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. The Bunch will contain a
frameattribute with the target and the data. If
return_X_yis True, then
(data, target)will be pandas DataFrames or Series as describe above.
Changed in version 0.9.0: Default value changed to True.
return_X_y (bool, default=False) – If True, returns
(data.data, data.target)instead of a Bunch object.
warn (bool, default=True) – If True, it raises an extra warning to make users aware of the unfairness aspect of this dataset.
Bunch) – Dictionary-like object, with the following attributes.
- datandarray, shape (506, 13)
Each row corresponding to the 13 feature values in order. If
datais a pandas object.
- targetnumpy array of shape (506,)
Each value corresponds to the average house value in units of 100,000. If
targetis a pandas object.
- feature_nameslist of length 13
Array of ordered feature names used in the dataset.
Description of the Boston housing dataset.
- categoriesdict or None
Maps each categorical feature name to a list of values, such that the value encoded as i is ith in the list. If
as_frameis True, this is None.
- framepandas DataFrame
Only present when
as_frameis True. DataFrame with
(data, target) (tuple if
Our API largely follows the API of
sklearn.datasets.fetch_openml(). This dataset consists of 506 samples and 13 features. It is notorious for the fairness issues related to the B column. There’s more information in the references.