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<div class="section" id="toy-datasets">
<span id="id1"></span><h1><span class="section-number">7.1. </span>Toy datasets<a class="headerlink" href="#toy-datasets" title="Permalink to this headline">¶</a></h1>
<p>scikit-learn comes with a few small standard datasets that do not require to
download any file from some external website.</p>
<p>They can be loaded using the following functions:</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston" title="sklearn.datasets.load_boston"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_boston</span></code></a>(*[, return_X_y])</p></td>
<td><p>Load and return the boston house-prices dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_iris</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the iris dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes" title="sklearn.datasets.load_diabetes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_diabetes</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the diabetes dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_digits</span></code></a>(*[, n_class, return_X_y, as_frame])</p></td>
<td><p>Load and return the digits dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud" title="sklearn.datasets.load_linnerud"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_linnerud</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the physical excercise linnerud dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine" title="sklearn.datasets.load_wine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_wine</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the wine dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer" title="sklearn.datasets.load_breast_cancer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_breast_cancer</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the breast cancer wisconsin dataset (classification).</p></td>
</tr>
</tbody>
</table>
<p>These datasets are useful to quickly illustrate the behavior of the
various algorithms implemented in scikit-learn. They are however often too
small to be representative of real world machine learning tasks.</p>
<div class="section" id="boston-house-prices-dataset">
<span id="boston-dataset"></span><h2><span class="section-number">7.1.1. </span>Boston house prices dataset<a class="headerlink" href="#boston-house-prices-dataset" title="Permalink to this headline">¶</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>506</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.</p>
</dd>
<dt class="field-odd">Attribute Information (in order)</dt>
<dd class="field-odd"><ul class="simple">
<li><p>CRIM per capita crime rate by town</p></li>
<li><p>ZN proportion of residential land zoned for lots over 25,000 sq.ft.</p></li>
<li><p>INDUS proportion of non-retail business acres per town</p></li>
<li><p>CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)</p></li>
<li><p>NOX nitric oxides concentration (parts per 10 million)</p></li>
<li><p>RM average number of rooms per dwelling</p></li>
<li><p>AGE proportion of owner-occupied units built prior to 1940</p></li>
<li><p>DIS weighted distances to five Boston employment centres</p></li>
<li><p>RAD index of accessibility to radial highways</p></li>
<li><p>TAX full-value property-tax rate per $10,000</p></li>
<li><p>PTRATIO pupil-teacher ratio by town</p></li>
<li><p>B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town</p></li>
<li><p>LSTAT % lower status of the population</p></li>
<li><p>MEDV Median value of owner-occupied homes in $1000’s</p></li>
</ul>
</dd>
<dt class="field-even">Missing Attribute Values</dt>
<dd class="field-even"><p>None</p>
</dd>
<dt class="field-odd">Creator</dt>
<dd class="field-odd"><p>Harrison, D. and Rubinfeld, D.L.</p>
</dd>
</dl>
</div></blockquote>
<p>This is a copy of UCI ML housing dataset.
<a class="reference external" href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/">https://archive.ics.uci.edu/ml/machine-learning-databases/housing/</a></p>
<p>This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.</p>
<p>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.</p>
<p>The Boston house-price data has been used in many machine learning papers that address regression
problems.</p>
<div class="topic">
<p class="topic-title">References</p>
<ul class="simple">
<li><p>Belsley, Kuh & Welsch, ‘Regression diagnostics: Identifying Influential Data and Sources of Collinearity’, Wiley, 1980. 244-261.</p></li>
<li><p>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.</p></li>
</ul>
</div>
</div>
<div class="section" id="iris-plants-dataset">
<span id="iris-dataset"></span><h2><span class="section-number">7.1.2. </span>Iris plants dataset<a class="headerlink" href="#iris-plants-dataset" title="Permalink to this headline">¶</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>150 (50 in each of three classes)</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>4 numeric, predictive attributes and the class</p>
</dd>
<dt class="field-odd">Attribute Information</dt>
<dd class="field-odd"><ul class="simple">
<li><p>sepal length in cm</p></li>
<li><p>sepal width in cm</p></li>
<li><p>petal length in cm</p></li>
<li><p>petal width in cm</p></li>
<li><dl class="simple">
<dt>class:</dt><dd><ul>
<li><p>Iris-Setosa</p></li>
<li><p>Iris-Versicolour</p></li>
<li><p>Iris-Virginica</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
</dd>
<dt class="field-even">Summary Statistics</dt>
<dd class="field-even"><p></p></dd>
</dl>
<table class="docutils align-default">
<colgroup>
<col style="width: 26%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 13%" />
<col style="width: 9%" />
<col style="width: 37%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>sepal length:</p></td>
<td><p>4.3</p></td>
<td><p>7.9</p></td>
<td><p>5.84</p></td>
<td><p>0.83</p></td>
<td><p>0.7826</p></td>
</tr>
<tr class="row-odd"><td><p>sepal width:</p></td>
<td><p>2.0</p></td>
<td><p>4.4</p></td>
<td><p>3.05</p></td>
<td><p>0.43</p></td>
<td><p>-0.4194</p></td>
</tr>
<tr class="row-even"><td><p>petal length:</p></td>
<td><p>1.0</p></td>
<td><p>6.9</p></td>
<td><p>3.76</p></td>
<td><p>1.76</p></td>
<td><p>0.9490 (high!)</p></td>
</tr>
<tr class="row-odd"><td><p>petal width:</p></td>
<td><p>0.1</p></td>
<td><p>2.5</p></td>
<td><p>1.20</p></td>
<td><p>0.76</p></td>
<td><p>0.9565 (high!)</p></td>
</tr>
</tbody>
</table>
<dl class="field-list simple">
<dt class="field-odd">Missing Attribute Values</dt>
<dd class="field-odd"><p>None</p>
</dd>
<dt class="field-even">Class Distribution</dt>
<dd class="field-even"><p>33.3% for each of 3 classes.</p>
</dd>
<dt class="field-odd">Creator</dt>
<dd class="field-odd"><p>R.A. Fisher</p>
</dd>
<dt class="field-even">Donor</dt>
<dd class="field-even"><p>Michael Marshall (<a class="reference external" href="mailto:MARSHALL%PLU%40io.arc.nasa.gov">MARSHALL%PLU<span>@</span>io<span>.</span>arc<span>.</span>nasa<span>.</span>gov</a>)</p>
</dd>
<dt class="field-odd">Date</dt>
<dd class="field-odd"><p>July, 1988</p>
</dd>
</dl>
</div></blockquote>
<p>The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher’s paper. Note that it’s the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.</p>
<p>This is perhaps the best known database to be found in the
pattern recognition literature. Fisher’s paper is a classic in the field and
is referenced frequently to this day. (See Duda & Hart, for example.) The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant. One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.</p>
<div class="topic">
<p class="topic-title">References</p>
<ul class="simple">
<li><p>Fisher, R.A. “The use of multiple measurements in taxonomic problems”
Annual Eugenics, 7, Part II, 179-188 (1936); also in “Contributions to
Mathematical Statistics” (John Wiley, NY, 1950).</p></li>
<li><p>Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.</p></li>
<li><p>Dasarathy, B.V. (1980) “Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments”. IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.</p></li>
<li><p>Gates, G.W. (1972) “The Reduced Nearest Neighbor Rule”. IEEE Transactions
on Information Theory, May 1972, 431-433.</p></li>
<li><p>See also: 1988 MLC Proceedings, 54-64. Cheeseman et al”s AUTOCLASS II
conceptual clustering system finds 3 classes in the data.</p></li>
<li><p>Many, many more …</p></li>
</ul>
</div>
</div>
<div class="section" id="diabetes-dataset">
<span id="id2"></span><h2><span class="section-number">7.1.3. </span>Diabetes dataset<a class="headerlink" href="#diabetes-dataset" title="Permalink to this headline">¶</a></h2>
<p>Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after baseline.</p>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>442</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>First 10 columns are numeric predictive values</p>
</dd>
<dt class="field-odd">Target</dt>
<dd class="field-odd"><p>Column 11 is a quantitative measure of disease progression one year after baseline</p>
</dd>
<dt class="field-even">Attribute Information</dt>
<dd class="field-even"><ul class="simple">
<li><p>age age in years</p></li>
<li><p>sex</p></li>
<li><p>bmi body mass index</p></li>
<li><p>bp average blood pressure</p></li>
<li><p>s1 tc, T-Cells (a type of white blood cells)</p></li>
<li><p>s2 ldl, low-density lipoproteins</p></li>
<li><p>s3 hdl, high-density lipoproteins</p></li>
<li><p>s4 tch, thyroid stimulating hormone</p></li>
<li><p>s5 ltg, lamotrigine</p></li>
<li><p>s6 glu, blood sugar level</p></li>
</ul>
</dd>
</dl>
</div></blockquote>
<p>Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> (i.e. the sum of squares of each column totals 1).</p>
<p>Source URL:
<a class="reference external" href="https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html">https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html</a></p>
<p>For more information see:
Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) “Least Angle Regression,” Annals of Statistics (with discussion), 407-499.
(<a class="reference external" href="https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf">https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf</a>)</p>
</div>
<div class="section" id="optical-recognition-of-handwritten-digits-dataset">
<span id="digits-dataset"></span><h2><span class="section-number">7.1.4. </span>Optical recognition of handwritten digits dataset<a class="headerlink" href="#optical-recognition-of-handwritten-digits-dataset" title="Permalink to this headline">¶</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>1797</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>64</p>
</dd>
<dt class="field-odd">Attribute Information</dt>
<dd class="field-odd"><p>8x8 image of integer pixels in the range 0..16.</p>
</dd>
<dt class="field-even">Missing Attribute Values</dt>
<dd class="field-even"><p>None</p>
</dd>
<dt class="field-odd">Creator</dt>
<dd class="field-odd"><ol class="upperalpha simple" start="5">
<li><p>Alpaydin (alpaydin ‘@’ boun.edu.tr)</p></li>
</ol>
</dd>
<dt class="field-even">Date</dt>
<dd class="field-even"><p>July; 1998</p>
</dd>
</dl>
</div></blockquote>
<p>This is a copy of the test set of the UCI ML hand-written digits datasets
<a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits</a></p>
<p>The data set contains images of hand-written digits: 10 classes where
each class refers to a digit.</p>
<p>Preprocessing programs made available by NIST were used to extract
normalized bitmaps of handwritten digits from a preprinted form. From a
total of 43 people, 30 contributed to the training set and different 13
to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
4x4 and the number of on pixels are counted in each block. This generates
an input matrix of 8x8 where each element is an integer in the range
0..16. This reduces dimensionality and gives invariance to small
distortions.</p>
<p>For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
1994.</p>
<div class="topic">
<p class="topic-title">References</p>
<ul class="simple">
<li><p>C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
Graduate Studies in Science and Engineering, Bogazici University.</p></li>
<li><ol class="upperalpha simple" start="5">
<li><p>Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.</p></li>
</ol>
</li>
<li><p>Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
Linear dimensionalityreduction using relevance weighted LDA. School of
Electrical and Electronic Engineering Nanyang Technological University.
2005.</p></li>
<li><p>Claudio Gentile. A New Approximate Maximal Margin Classification
Algorithm. NIPS. 2000.</p></li>
</ul>
</div>
</div>
<div class="section" id="linnerrud-dataset">
<span id="id3"></span><h2><span class="section-number">7.1.5. </span>Linnerrud dataset<a class="headerlink" href="#linnerrud-dataset" title="Permalink to this headline">¶</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>20</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>3</p>
</dd>
<dt class="field-odd">Missing Attribute Values</dt>
<dd class="field-odd"><p>None</p>
</dd>
</dl>
</div></blockquote>
<p>The Linnerud dataset is a multi-output regression dataset. It consists of three
excercise (data) and three physiological (target) variables collected from
twenty middle-aged men in a fitness club:</p>
<ul class="simple">
<li><dl class="simple">
<dt><em>physiological</em> - CSV containing 20 observations on 3 physiological variables:</dt><dd><p>Weight, Waist and Pulse.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><em>exercise</em> - CSV containing 20 observations on 3 exercise variables:</dt><dd><p>Chins, Situps and Jumps.</p>
</dd>
</dl>
</li>
</ul>
<div class="topic">
<p class="topic-title">References</p>
<ul class="simple">
<li><p>Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
Editions Technic.</p></li>
</ul>
</div>
</div>
<div class="section" id="wine-recognition-dataset">
<span id="wine-dataset"></span><h2><span class="section-number">7.1.6. </span>Wine recognition dataset<a class="headerlink" href="#wine-recognition-dataset" title="Permalink to this headline">¶</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>178 (50 in each of three classes)</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>13 numeric, predictive attributes and the class</p>
</dd>
<dt class="field-odd">Attribute Information</dt>
<dd class="field-odd"><ul class="simple">
<li><p>Alcohol</p></li>
<li><p>Malic acid</p></li>
<li><p>Ash</p></li>
<li><p>Alcalinity of ash</p></li>
<li><p>Magnesium</p></li>
<li><p>Total phenols</p></li>
<li><p>Flavanoids</p></li>
<li><p>Nonflavanoid phenols</p></li>
<li><p>Proanthocyanins</p></li>
<li><p>Color intensity</p></li>
<li><p>Hue</p></li>
<li><p>OD280/OD315 of diluted wines</p></li>
<li><p>Proline</p></li>
</ul>
</dd>
</dl>
<ul class="simple">
<li><dl class="simple">
<dt>class:</dt><dd><ul>
<li><p>class_0</p></li>
<li><p>class_1</p></li>
<li><p>class_2</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Summary Statistics</dt>
<dd class="field-odd"><p></p></dd>
</dl>
<table class="docutils align-default">
<colgroup>
<col style="width: 58%" />
<col style="width: 8%" />
<col style="width: 10%" />
<col style="width: 14%" />
<col style="width: 10%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Alcohol:</p></td>
<td><p>11.0</p></td>
<td><p>14.8</p></td>
<td><p>13.0</p></td>
<td><p>0.8</p></td>
</tr>
<tr class="row-odd"><td><p>Malic Acid:</p></td>
<td><p>0.74</p></td>
<td><p>5.80</p></td>
<td><p>2.34</p></td>
<td><p>1.12</p></td>
</tr>
<tr class="row-even"><td><p>Ash:</p></td>
<td><p>1.36</p></td>
<td><p>3.23</p></td>
<td><p>2.36</p></td>
<td><p>0.27</p></td>
</tr>
<tr class="row-odd"><td><p>Alcalinity of Ash:</p></td>
<td><p>10.6</p></td>
<td><p>30.0</p></td>
<td><p>19.5</p></td>
<td><p>3.3</p></td>
</tr>
<tr class="row-even"><td><p>Magnesium:</p></td>
<td><p>70.0</p></td>
<td><p>162.0</p></td>
<td><p>99.7</p></td>
<td><p>14.3</p></td>
</tr>
<tr class="row-odd"><td><p>Total Phenols:</p></td>
<td><p>0.98</p></td>
<td><p>3.88</p></td>
<td><p>2.29</p></td>
<td><p>0.63</p></td>
</tr>
<tr class="row-even"><td><p>Flavanoids:</p></td>
<td><p>0.34</p></td>
<td><p>5.08</p></td>
<td><p>2.03</p></td>
<td><p>1.00</p></td>
</tr>
<tr class="row-odd"><td><p>Nonflavanoid Phenols:</p></td>
<td><p>0.13</p></td>
<td><p>0.66</p></td>
<td><p>0.36</p></td>
<td><p>0.12</p></td>
</tr>
<tr class="row-even"><td><p>Proanthocyanins:</p></td>
<td><p>0.41</p></td>
<td><p>3.58</p></td>
<td><p>1.59</p></td>
<td><p>0.57</p></td>
</tr>
<tr class="row-odd"><td><p>Colour Intensity:</p></td>
<td><p>1.3</p></td>
<td><p>13.0</p></td>
<td><p>5.1</p></td>
<td><p>2.3</p></td>
</tr>
<tr class="row-even"><td><p>Hue:</p></td>
<td><p>0.48</p></td>
<td><p>1.71</p></td>
<td><p>0.96</p></td>
<td><p>0.23</p></td>
</tr>
<tr class="row-odd"><td><p>OD280/OD315 of diluted wines:</p></td>
<td><p>1.27</p></td>
<td><p>4.00</p></td>
<td><p>2.61</p></td>
<td><p>0.71</p></td>
</tr>
<tr class="row-even"><td><p>Proline:</p></td>
<td><p>278</p></td>
<td><p>1680</p></td>
<td><p>746</p></td>
<td><p>315</p></td>
</tr>
</tbody>
</table>
<dl class="field-list simple">
<dt class="field-odd">Missing Attribute Values</dt>
<dd class="field-odd"><p>None</p>
</dd>
<dt class="field-even">Class Distribution</dt>
<dd class="field-even"><p>class_0 (59), class_1 (71), class_2 (48)</p>
</dd>
<dt class="field-odd">Creator</dt>
<dd class="field-odd"><p>R.A. Fisher</p>
</dd>
<dt class="field-even">Donor</dt>
<dd class="field-even"><p>Michael Marshall (<a class="reference external" href="mailto:MARSHALL%PLU%40io.arc.nasa.gov">MARSHALL%PLU<span>@</span>io<span>.</span>arc<span>.</span>nasa<span>.</span>gov</a>)</p>
</dd>
<dt class="field-odd">Date</dt>
<dd class="field-odd"><p>July, 1988</p>
</dd>
</dl>
</div></blockquote>
<p>This is a copy of UCI ML Wine recognition datasets.
<a class="reference external" href="https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data">https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data</a></p>
<p>The data is the results of a chemical analysis of wines grown in the same
region in Italy by three different cultivators. There are thirteen different
measurements taken for different constituents found in the three types of
wine.</p>
<p>Original Owners:</p>
<p>Forina, M. et al, PARVUS -
An Extendible Package for Data Exploration, Classification and Correlation.
Institute of Pharmaceutical and Food Analysis and Technologies,
Via Brigata Salerno, 16147 Genoa, Italy.</p>
<p>Citation:</p>
<p>Lichman, M. (2013). UCI Machine Learning Repository
[<a class="reference external" href="https://archive.ics.uci.edu/ml">https://archive.ics.uci.edu/ml</a>]. Irvine, CA: University of California,
School of Information and Computer Science.</p>
<div class="topic">
<p class="topic-title">References</p>
<p>(1) S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Technometrics).</p>
<p>The data was used with many others for comparing various
classifiers. The classes are separable, though only RDA
has achieved 100% correct classification.
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
(All results using the leave-one-out technique)</p>
<p>(2) S. Aeberhard, D. Coomans and O. de Vel,
“THE CLASSIFICATION PERFORMANCE OF RDA”
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Journal of Chemometrics).</p>
</div>
</div>
<div class="section" id="breast-cancer-wisconsin-diagnostic-dataset">
<span id="breast-cancer-dataset"></span><h2><span class="section-number">7.1.7. </span>Breast cancer wisconsin (diagnostic) dataset<a class="headerlink" href="#breast-cancer-wisconsin-diagnostic-dataset" title="Permalink to this headline">¶</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<blockquote>
<div><dl class="field-list">
<dt class="field-odd">Number of Instances</dt>
<dd class="field-odd"><p>569</p>
</dd>
<dt class="field-even">Number of Attributes</dt>
<dd class="field-even"><p>30 numeric, predictive attributes and the class</p>
</dd>
<dt class="field-odd">Attribute Information</dt>
<dd class="field-odd"><ul class="simple">
<li><p>radius (mean of distances from center to points on the perimeter)</p></li>
<li><p>texture (standard deviation of gray-scale values)</p></li>
<li><p>perimeter</p></li>
<li><p>area</p></li>
<li><p>smoothness (local variation in radius lengths)</p></li>
<li><p>compactness (perimeter^2 / area - 1.0)</p></li>
<li><p>concavity (severity of concave portions of the contour)</p></li>
<li><p>concave points (number of concave portions of the contour)</p></li>
<li><p>symmetry</p></li>
<li><p>fractal dimension (“coastline approximation” - 1)</p></li>
</ul>
<p>The mean, standard error, and “worst” or largest (mean of the three
worst/largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 0 is Mean Radius, field
10 is Radius SE, field 20 is Worst Radius.</p>
<ul class="simple">
<li><dl class="simple">
<dt>class:</dt><dd><ul>
<li><p>WDBC-Malignant</p></li>
<li><p>WDBC-Benign</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
</dd>
<dt class="field-even">Summary Statistics</dt>
<dd class="field-even"><p></p></dd>
</dl>
<table class="docutils align-default">
<colgroup>
<col style="width: 76%" />
<col style="width: 12%" />
<col style="width: 12%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>radius (mean):</p></td>
<td><p>6.981</p></td>
<td><p>28.11</p></td>
</tr>
<tr class="row-odd"><td><p>texture (mean):</p></td>
<td><p>9.71</p></td>
<td><p>39.28</p></td>
</tr>
<tr class="row-even"><td><p>perimeter (mean):</p></td>
<td><p>43.79</p></td>
<td><p>188.5</p></td>
</tr>
<tr class="row-odd"><td><p>area (mean):</p></td>
<td><p>143.5</p></td>
<td><p>2501.0</p></td>
</tr>
<tr class="row-even"><td><p>smoothness (mean):</p></td>
<td><p>0.053</p></td>
<td><p>0.163</p></td>
</tr>
<tr class="row-odd"><td><p>compactness (mean):</p></td>
<td><p>0.019</p></td>
<td><p>0.345</p></td>
</tr>
<tr class="row-even"><td><p>concavity (mean):</p></td>
<td><p>0.0</p></td>
<td><p>0.427</p></td>
</tr>
<tr class="row-odd"><td><p>concave points (mean):</p></td>
<td><p>0.0</p></td>
<td><p>0.201</p></td>
</tr>
<tr class="row-even"><td><p>symmetry (mean):</p></td>
<td><p>0.106</p></td>
<td><p>0.304</p></td>
</tr>
<tr class="row-odd"><td><p>fractal dimension (mean):</p></td>
<td><p>0.05</p></td>
<td><p>0.097</p></td>
</tr>
<tr class="row-even"><td><p>radius (standard error):</p></td>
<td><p>0.112</p></td>
<td><p>2.873</p></td>
</tr>
<tr class="row-odd"><td><p>texture (standard error):</p></td>
<td><p>0.36</p></td>
<td><p>4.885</p></td>
</tr>
<tr class="row-even"><td><p>perimeter (standard error):</p></td>
<td><p>0.757</p></td>
<td><p>21.98</p></td>
</tr>
<tr class="row-odd"><td><p>area (standard error):</p></td>
<td><p>6.802</p></td>
<td><p>542.2</p></td>
</tr>
<tr class="row-even"><td><p>smoothness (standard error):</p></td>
<td><p>0.002</p></td>
<td><p>0.031</p></td>
</tr>
<tr class="row-odd"><td><p>compactness (standard error):</p></td>
<td><p>0.002</p></td>
<td><p>0.135</p></td>
</tr>
<tr class="row-even"><td><p>concavity (standard error):</p></td>
<td><p>0.0</p></td>
<td><p>0.396</p></td>
</tr>
<tr class="row-odd"><td><p>concave points (standard error):</p></td>
<td><p>0.0</p></td>
<td><p>0.053</p></td>
</tr>
<tr class="row-even"><td><p>symmetry (standard error):</p></td>
<td><p>0.008</p></td>
<td><p>0.079</p></td>
</tr>
<tr class="row-odd"><td><p>fractal dimension (standard error):</p></td>
<td><p>0.001</p></td>
<td><p>0.03</p></td>
</tr>
<tr class="row-even"><td><p>radius (worst):</p></td>
<td><p>7.93</p></td>
<td><p>36.04</p></td>
</tr>
<tr class="row-odd"><td><p>texture (worst):</p></td>
<td><p>12.02</p></td>
<td><p>49.54</p></td>
</tr>
<tr class="row-even"><td><p>perimeter (worst):</p></td>
<td><p>50.41</p></td>
<td><p>251.2</p></td>
</tr>
<tr class="row-odd"><td><p>area (worst):</p></td>
<td><p>185.2</p></td>
<td><p>4254.0</p></td>
</tr>
<tr class="row-even"><td><p>smoothness (worst):</p></td>
<td><p>0.071</p></td>
<td><p>0.223</p></td>
</tr>
<tr class="row-odd"><td><p>compactness (worst):</p></td>
<td><p>0.027</p></td>
<td><p>1.058</p></td>
</tr>
<tr class="row-even"><td><p>concavity (worst):</p></td>
<td><p>0.0</p></td>
<td><p>1.252</p></td>
</tr>
<tr class="row-odd"><td><p>concave points (worst):</p></td>
<td><p>0.0</p></td>
<td><p>0.291</p></td>
</tr>
<tr class="row-even"><td><p>symmetry (worst):</p></td>
<td><p>0.156</p></td>
<td><p>0.664</p></td>
</tr>
<tr class="row-odd"><td><p>fractal dimension (worst):</p></td>
<td><p>0.055</p></td>
<td><p>0.208</p></td>
</tr>
</tbody>
</table>
<dl class="field-list simple">
<dt class="field-odd">Missing Attribute Values</dt>
<dd class="field-odd"><p>None</p>
</dd>
<dt class="field-even">Class Distribution</dt>
<dd class="field-even"><p>212 - Malignant, 357 - Benign</p>
</dd>
<dt class="field-odd">Creator</dt>