diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst
index 88b1a4b517b36f213f05e2f273a79da83904070b..d864dd1c9b479c782f3f1e7204b2c699cda178da 100644
--- a/doc/datasets/index.rst
+++ b/doc/datasets/index.rst
@@ -38,6 +38,7 @@ require to download any file from some external website.
:toctree: generated/
:template: function.rst
+ load_boston
load_iris
load_diabetes
load_digits
diff --git a/doc/tutorial.rst b/doc/tutorial.rst
index a9119c4ebc0f2082f51869ce6aa4b1b933b31854..ad6709cb8e80a6fac2216fd9b22232d516aab27f 100644
--- a/doc/tutorial.rst
+++ b/doc/tutorial.rst
@@ -58,9 +58,10 @@ Loading an example dataset
--------------------------
`scikits.learn` comes with a few standard datasets, for instance the
-`iris dataset <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_, or
-the `digits dataset
-<http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits>`_::
+`iris <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ and `digits
+<http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits>`_
+datasets for classification and the `boston house prices dataset
+<http://archive.ics.uci.edu/ml/datasets/Housing>`_ for regression.::
>>> from scikits.learn import datasets
>>> iris = datasets.load_iris()
diff --git a/scikits/learn/datasets/base.py b/scikits/learn/datasets/base.py
index 9447a4581735e2629c7091a2e29ea5cc26d12146..62c0699974a515f1509556fe4994f8664943583b 100644
--- a/scikits/learn/datasets/base.py
+++ b/scikits/learn/datasets/base.py
@@ -320,7 +320,7 @@ def load_linnerud():
def load_boston():
- """Load the Boston house prices dataset and return it.
+ """Load and return the boston house-prices dataset (regression).
Returns
-------
diff --git a/scikits/learn/datasets/descr/boston_house_prices.rst b/scikits/learn/datasets/descr/boston_house_prices.rst
index c0c8b29c551980552f0e73d6caa831c3eab96866..804e0e01554216c180ac37d1e6b37a1bb02bc5bc 100644
--- a/scikits/learn/datasets/descr/boston_house_prices.rst
+++ b/scikits/learn/datasets/descr/boston_house_prices.rst
@@ -1,39 +1,53 @@
Boston House Prices dataset
-Source
+Notes
------
- http://lib.stat.cmu.edu/datasets/boston
+Data Set Characteristics:
- The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
- prices and the demand for clean air', J. Environ. Economics & Management,
- vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
- ...', Wiley, 1980. N.B. Various transformations are used in the table on
- pages 244-261 of the latter.
+ :Number of Instances: 506
+ :Number of Attributes: 13 numeric/categorical predictive
+
+ :Median Value (attribute 14) is usually the target
+ :Attribute Information (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 blacks by town
+ - LSTAT % lower status of the population
+ - MEDV Median value of owner-occupied homes in $1000's
+
+ :Missing Attribute Values: None
+
+ :Creator: Harrison, D. and Rubinfeld, D.L.
+
+This is a copy of UCI ML housing dataset.
+http://archive.ics.uci.edu/ml/datasets/Housing
+
+
+This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
+
+The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
+prices and the demand for clean air', J. Environ. Economics & Management,
+vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
+...', Wiley, 1980. N.B. Various transformations are used in the table on
+pages 244-261 of the latter.
+
+The Boston house-price data has been used in many machine learning papers that address regression
+problems.
-Number of Instances: 452
-
-Number of Attributes: 14 numeric, predictive attributes
-
-Attribute 14 (Median Value) is usually the target
-
-Attribute Information:
- 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 blacks by town
- LSTAT % lower status of the population
- MEDV Median value of owner-occupied homes in $1000's
-
-Summary Statistics:
- TODO
+References
+----------
+
+ - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
+ - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
+ - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)