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:

OpenML [1]
Paper: Harrison and Rubinfeld [2]

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:

cachebool, default=True: Whether to cache downloaded datasets using joblib.
data_homestr, default=None: Specify another download and cache folder for the datasets. By default, all fairlearn data is stored in ‘~/.fairlearn-data’ subfolders.
as_framebool, 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. If return_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_ybool, default=False: If True, returns (data.data, data.target) instead of a Bunch object.
warnbool, default=True: If True, it raises an extra warning to make users aware of the unfairness aspect of this dataset.

Returns:

datasetBunch

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 with data and target.

(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