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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/mixture.html">2.1. Gaussian mixture models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="loading_other_datasets.html">7.4. Loading other datasets</a></li>
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</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<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="Link to this heading">#</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>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><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-even"><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, scaled])</p></td>
<td><p>Load and return the diabetes dataset (regression).</p></td>
</tr>
<tr class="row-odd"><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-even"><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 exercise Linnerud dataset.</p></td>
</tr>
<tr class="row-odd"><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-even"><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>
</div>
<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>
<section id="iris-plants-dataset">
<span id="iris-dataset"></span><h2><span class="section-number">7.1.1. </span>Iris plants dataset<a class="headerlink" href="#iris-plants-dataset" title="Link to this heading">#</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<dl class="field-list simple">
<dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
<dd class="field-odd"><p>150 (50 in each of three classes)</p>
</dd>
<dt class="field-even">Number of Attributes<span class="colon">:</span></dt>
<dd class="field-even"><p>4 numeric, predictive attributes and the class</p>
</dd>
<dt class="field-odd">Attribute Information<span class="colon">:</span></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<span class="colon">:</span></dt>
<dd class="field-even"><p></p></dd>
</dl>
<div class="pst-scrollable-table-container"><table class="table">
<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>
</div>
<dl class="field-list simple">
<dt class="field-odd">Missing Attribute Values<span class="colon">:</span></dt>
<dd class="field-odd"><p>None</p>
</dd>
<dt class="field-even">Class Distribution<span class="colon">:</span></dt>
<dd class="field-even"><p>33.3% for each of 3 classes.</p>
</dd>
<dt class="field-odd">Creator<span class="colon">:</span></dt>
<dd class="field-odd"><p>R.A. Fisher</p>
</dd>
<dt class="field-even">Donor<span class="colon">:</span></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<span class="colon">:</span></dt>
<dd class="field-odd"><p>July, 1988</p>
</dd>
</dl>
<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>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="references">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">References<a class="headerlink" href="#references" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<ul class="simple">
<li><p class="sd-card-text">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 class="sd-card-text">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 class="sd-card-text">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 class="sd-card-text">Gates, G.W. (1972) “The Reduced Nearest Neighbor Rule”. IEEE Transactions
on Information Theory, May 1972, 431-433.</p></li>
<li><p class="sd-card-text">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 class="sd-card-text">Many, many more …</p></li>
</ul>
</div>
</details></section>
<section id="diabetes-dataset">
<span id="id2"></span><h2><span class="section-number">7.1.2. </span>Diabetes dataset<a class="headerlink" href="#diabetes-dataset" title="Link to this heading">#</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>
<dl class="field-list simple">
<dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
<dd class="field-odd"><p>442</p>
</dd>
<dt class="field-even">Number of Attributes<span class="colon">:</span></dt>
<dd class="field-even"><p>First 10 columns are numeric predictive values</p>
</dd>
<dt class="field-odd">Target<span class="colon">:</span></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<span class="colon">:</span></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, total serum cholesterol</p></li>
<li><p>s2 ldl, low-density lipoproteins</p></li>
<li><p>s3 hdl, high-density lipoproteins</p></li>
<li><p>s4 tch, total cholesterol / HDL</p></li>
<li><p>s5 ltg, possibly log of serum triglycerides level</p></li>
<li><p>s6 glu, blood sugar level</p></li>
</ul>
</dd>
</dl>
<p>Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of <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>
</section>
<section id="optical-recognition-of-handwritten-digits-dataset">
<span id="digits-dataset"></span><h2><span class="section-number">7.1.3. </span>Optical recognition of handwritten digits dataset<a class="headerlink" href="#optical-recognition-of-handwritten-digits-dataset" title="Link to this heading">#</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<dl class="field-list simple">
<dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
<dd class="field-odd"><p>1797</p>
</dd>
<dt class="field-even">Number of Attributes<span class="colon">:</span></dt>
<dd class="field-even"><p>64</p>
</dd>
<dt class="field-odd">Attribute Information<span class="colon">:</span></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<span class="colon">:</span></dt>
<dd class="field-even"><p>None</p>
</dd>
<dt class="field-odd">Creator<span class="colon">:</span></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<span class="colon">:</span></dt>
<dd class="field-even"><p>July; 1998</p>
</dd>
</dl>
<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>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="references-2">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">References<a class="headerlink" href="#references-2" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<ul class="simple">
<li><p class="sd-card-text">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 class="sd-card-text">Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.</p></li>
</ol>
</li>
<li><p class="sd-card-text">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 class="sd-card-text">Claudio Gentile. A New Approximate Maximal Margin Classification
Algorithm. NIPS. 2000.</p></li>
</ul>
</div>
</details></section>
<section id="linnerrud-dataset">
<span id="id3"></span><h2><span class="section-number">7.1.4. </span>Linnerrud dataset<a class="headerlink" href="#linnerrud-dataset" title="Link to this heading">#</a></h2>
<p><strong>Data Set Characteristics:</strong></p>
<dl class="field-list simple">
<dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
<dd class="field-odd"><p>20</p>
</dd>
<dt class="field-even">Number of Attributes<span class="colon">:</span></dt>
<dd class="field-even"><p>3</p>
</dd>
<dt class="field-odd">Missing Attribute Values<span class="colon">:</span></dt>
<dd class="field-odd"><p>None</p>
</dd>
</dl>
<p>The Linnerud dataset is a multi-output regression dataset. It consists of three
exercise (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>