fairlearn.datasets.fetch_boston#
- fairlearn.datasets.fetch_boston(*, cache=True, data_home=None, as_frame=True, return_X_y=False, warn=True)[source]#
Load the boston housing dataset (regression).
Download it if necessary.
Samples total
506
Dimensionality
13
Features
real
Target
real 5. - 50.
Source:
The Boston house-price data of D. Harrison, and D.L. Rubinfeld [2].
Referenced in Belsley, Kuh & Welsch [3].
This dataset has known fairness issues [4]. 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) [5]. See the references at the bottom for more detailed information.
Here’s a table of all the variables in order:
CRIM
per capita crime rate by town
ZN
proportion of residential land zoned for lots over 25,000 sq.ft.
INDUS
proportion of non-retail business acres per town
CHAS
Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
NOX
nitric oxides concentration (parts per 10 million)
RM
average number of rooms per dwelling
AGE
proportion of owner-occupied units built prior to 1940
DIS
weighted distances to five Boston employment centres
RAD
index of accessibility to radial highways
TAX
full-value property-tax rate per $10,000
PTRATIO
pupil-teacher ratio by town
B
1000(Bk - 0.63)^2 where Bk is the proportion of Black people by town
LSTAT
% lower status of the population
MEDV
Median value of owner-occupied homes in $1000’s
Read more in the User Guide.
New in version 0.5.0.
- Parameters:
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
frame
attribute with the target and the data. Ifreturn_X_y
is 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.
- Returns:
dataset (
Bunch
) – Dictionary-like object, with the following attributes.- datandarray, shape (506, 13)
Each row corresponding to the 13 feature values in order. If
as_frame
is True,data
is a pandas object.- targetnumpy array of shape (506,)
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 13
Array of ordered feature names used in the dataset.
- DESCRstring
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_frame
is True, this is None.- framepandas DataFrame
Only present when
as_frame
is True. DataFrame withdata
andtarget
.
(data, target) (tuple if
return_X_y
is True)
Notes
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.References