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Commit 5c57694

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DOC Add dropdowns to module 7.1 Toy datasets (#26710)
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‎sklearn/datasets/descr/breast_cancer.rst

Copy file name to clipboardExpand all lines: sklearn/datasets/descr/breast_cancer.rst
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@@ -104,15 +104,19 @@ This database is also available through the UW CS ftp server:
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ftp ftp.cs.wisc.edu
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cd math-prog/cpo-dataset/machine-learn/WDBC/
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.. topic:: References
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- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
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for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
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Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
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San Jose, CA, 1993.
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- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
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prognosis via linear programming. Operations Research, 43(4), pages 570-577,
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July-August 1995.
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- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
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to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
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163-171.
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|details-start|
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**References**
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|details-split|
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- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
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for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
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Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
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San Jose, CA, 1993.
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- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
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prognosis via linear programming. Operations Research, 43(4), pages 570-577,
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July-August 1995.
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- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
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to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
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163-171.
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|details-end|

‎sklearn/datasets/descr/digits.rst

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@@ -32,15 +32,19 @@ T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
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L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
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1994.
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.. topic:: References
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- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
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Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
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Graduate Studies in Science and Engineering, Bogazici University.
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- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
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- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
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Linear dimensionalityreduction using relevance weighted LDA. School of
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Electrical and Electronic Engineering Nanyang Technological University.
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2005.
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- Claudio Gentile. A New Approximate Maximal Margin Classification
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Algorithm. NIPS. 2000.
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|details-start|
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**References**
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|details-split|
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- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
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Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
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Graduate Studies in Science and Engineering, Bogazici University.
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- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
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- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
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Linear dimensionalityreduction using relevance weighted LDA. School of
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Electrical and Electronic Engineering Nanyang Technological University.
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2005.
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- Claudio Gentile. A New Approximate Maximal Margin Classification
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Algorithm. NIPS. 2000.
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|details-end|

‎sklearn/datasets/descr/iris.rst

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@@ -45,19 +45,23 @@ data set contains 3 classes of 50 instances each, where each class refers to a
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type of iris plant. One class is linearly separable from the other 2; the
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latter are NOT linearly separable from each other.
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.. topic:: References
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- Fisher, R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
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Mathematical Statistics" (John Wiley, NY, 1950).
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- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
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on Information Theory, May 1972, 431-433.
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- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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- Many, many more ...
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|details-start|
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**References**
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|details-split|
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- Fisher, R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
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Mathematical Statistics" (John Wiley, NY, 1950).
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- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
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on Information Theory, May 1972, 431-433.
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- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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- Many, many more ...
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|details-end|

‎sklearn/datasets/descr/linnerud.rst

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@@ -18,7 +18,11 @@ twenty middle-aged men in a fitness club:
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- *exercise* - CSV containing 20 observations on 3 exercise variables:
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Chins, Situps and Jumps.
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.. topic:: References
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|details-start|
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**References**
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|details-split|
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* Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
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Editions Technic.
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* Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
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Editions Technic.
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|details-end|

‎sklearn/datasets/descr/wine_data.rst

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[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
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School of Information and Computer Science.
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.. topic:: References
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(1) S. Aeberhard, D. Coomans and O. de Vel,
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Comparison of Classifiers in High Dimensional Settings,
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Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
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Mathematics and Statistics, James Cook University of North Queensland.
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(Also submitted to Technometrics).
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The data was used with many others for comparing various
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classifiers. The classes are separable, though only RDA
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has achieved 100% correct classification.
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(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
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(All results using the leave-one-out technique)
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(2) S. Aeberhard, D. Coomans and O. de Vel,
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"THE CLASSIFICATION PERFORMANCE OF RDA"
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Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
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Mathematics and Statistics, James Cook University of North Queensland.
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(Also submitted to Journal of Chemometrics).
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|details-start|
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**References**
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|details-split|
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(1) S. Aeberhard, D. Coomans and O. de Vel,
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Comparison of Classifiers in High Dimensional Settings,
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Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
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Mathematics and Statistics, James Cook University of North Queensland.
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(Also submitted to Technometrics).
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The data was used with many others for comparing various
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classifiers. The classes are separable, though only RDA
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has achieved 100% correct classification.
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(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
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(All results using the leave-one-out technique)
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(2) S. Aeberhard, D. Coomans and O. de Vel,
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"THE CLASSIFICATION PERFORMANCE OF RDA"
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Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
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Mathematics and Statistics, James Cook University of North Queensland.
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(Also submitted to Journal of Chemometrics).
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|details-end|

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