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<a href="" class="sk-toc-active">6. Dataset transformations</a>
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<a href="modules/compose.html">6.1. Pipelines and composite estimators</a>
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</style><section id="dataset-transformations">
<span id="data-transforms"></span><h1><span class="section-number">6. </span>Dataset transformations<a class="headerlink" href="#dataset-transformations" title="Permalink to this heading">¶</a></h1>
<p>scikit-learn provides a library of transformers, which may clean (see
<a class="reference internal" href="modules/preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a>), reduce (see <a class="reference internal" href="modules/unsupervised_reduction.html#data-reduction"><span class="std std-ref">Unsupervised dimensionality reduction</span></a>), expand (see
<a class="reference internal" href="modules/kernel_approximation.html#kernel-approximation"><span class="std std-ref">Kernel Approximation</span></a>) or generate (see <a class="reference internal" href="modules/feature_extraction.html#feature-extraction"><span class="std std-ref">Feature extraction</span></a>)
feature representations.</p>
<p>Like other estimators, these are represented by classes with a <code class="docutils literal notranslate"><span class="pre">fit</span></code> method,
which learns model parameters (e.g. mean and standard deviation for
normalization) from a training set, and a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method which applies
this transformation model to unseen data. <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code> may be more
convenient and efficient for modelling and transforming the training data
simultaneously.</p>
<p>Combining such transformers, either in parallel or series is covered in
<a class="reference internal" href="modules/compose.html#combining-estimators"><span class="std std-ref">Pipelines and composite estimators</span></a>. <a class="reference internal" href="modules/metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a> covers transforming feature
spaces into affinity matrices, while <a class="reference internal" href="modules/preprocessing_targets.html#preprocessing-targets"><span class="std std-ref">Transforming the prediction target (y)</span></a> considers
transformations of the target space (e.g. categorical labels) for use in
scikit-learn.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#pipeline-chaining-estimators">6.1.1. Pipeline: chaining estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#transforming-target-in-regression">6.1.2. Transforming target in regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#featureunion-composite-feature-spaces">6.1.3. FeatureUnion: composite feature spaces</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#columntransformer-for-heterogeneous-data">6.1.4. ColumnTransformer for heterogeneous data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#visualizing-composite-estimators">6.1.5. Visualizing Composite Estimators</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#loading-features-from-dicts">6.2.1. Loading features from dicts</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#feature-hashing">6.2.2. Feature hashing</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#text-feature-extraction">6.2.3. Text feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#image-feature-extraction">6.2.4. Image feature extraction</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling">6.3.1. Standardization, or mean removal and variance scaling</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#non-linear-transformation">6.3.2. Non-linear transformation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#normalization">6.3.3. Normalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#encoding-categorical-features">6.3.4. Encoding categorical features</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#discretization">6.3.5. Discretization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#imputation-of-missing-values">6.3.6. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#generating-polynomial-features">6.3.7. Generating polynomial features</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#custom-transformers">6.3.8. Custom transformers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#univariate-vs-multivariate-imputation">6.4.1. Univariate vs. Multivariate Imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#univariate-feature-imputation">6.4.2. Univariate feature imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#multivariate-feature-imputation">6.4.3. Multivariate feature imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#references">6.4.4. References</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#nearest-neighbors-imputation">6.4.5. Nearest neighbors imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#marking-imputed-values">6.4.6. Marking imputed values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#estimators-that-handle-nan-values">6.4.7. Estimators that handle NaN values</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html#pca-principal-component-analysis">6.5.1. PCA: principal component analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html#random-projections">6.5.2. Random projections</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html#feature-agglomeration">6.5.3. Feature agglomeration</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#the-johnson-lindenstrauss-lemma">6.6.1. The Johnson-Lindenstrauss lemma</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#gaussian-random-projection">6.6.2. Gaussian random projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#sparse-random-projection">6.6.3. Sparse random projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#inverse-transform">6.6.4. Inverse Transform</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#nystroem-method-for-kernel-approximation">6.7.1. Nystroem Method for Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#radial-basis-function-kernel">6.7.2. Radial Basis Function Kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#additive-chi-squared-kernel">6.7.3. Additive Chi Squared Kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#skewed-chi-squared-kernel">6.7.4. Skewed Chi Squared Kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#polynomial-kernel-approximation-via-tensor-sketch">6.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#mathematical-details">6.7.6. Mathematical Details</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#cosine-similarity">6.8.1. Cosine similarity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#linear-kernel">6.8.2. Linear kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#polynomial-kernel">6.8.3. Polynomial kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#sigmoid-kernel">6.8.4. Sigmoid kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#rbf-kernel">6.8.5. RBF kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#laplacian-kernel">6.8.6. Laplacian kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#chi-squared-kernel">6.8.7. Chi-squared kernel</a></li>
</ul>
</li>
<li class="toctree-l1"><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><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html#label-binarization">6.9.1. Label binarization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html#label-encoding">6.9.2. Label encoding</a></li>
</ul>
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* copyable. */
var div = $('.highlight-python .highlight,' +
'.highlight-python3 .highlight,' +
'.highlight-pycon .highlight,' +
'.highlight-default .highlight')
var pre = div.find('pre');
// get the styles from the current theme
pre.parent().parent().css('position', 'relative');
var hide_text = 'Hide prompts and outputs';
var show_text = 'Show prompts and outputs';
// create and add the button to all the code blocks that contain >>>
div.each(function(index) {
var jthis = $(this);
if (jthis.find('.gp').length > 0) {
var button = $('<span class="copybutton">>>></span>');
button.attr('title', hide_text);
button.data('hidden', 'false');
jthis.prepend(button);
}
// tracebacks (.gt) contain bare text elements that need to be
// wrapped in a span to work with .nextUntil() (see later)
jthis.find('pre:has(.gt)').contents().filter(function() {
return ((this.nodeType == 3) && (this.data.trim().length > 0));
}).wrap('<span>');
});
// define the behavior of the button when it's clicked
$('.copybutton').click(function(e){
e.preventDefault();
var button = $(this);
if (button.data('hidden') === 'false') {
// hide the code output
button.parent().find('.go, .gp, .gt').hide();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
button.css('text-decoration', 'line-through');
button.attr('title', show_text);
button.data('hidden', 'true');
} else {
// show the code output
button.parent().find('.go, .gp, .gt').show();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
button.css('text-decoration', 'none');
button.attr('title', hide_text);
button.data('hidden', 'false');
}
});
/*** Add permalink buttons next to glossary terms ***/
$('dl.glossary > dt[id]').append(function() {
return ('<a class="headerlink" href="#' +
this.getAttribute('id') +
'" title="Permalink to this term">¶</a>');
});
/*** Hide navbar when scrolling down ***/
// Returns true when headerlink target matches hash in url
(function() {
hashTargetOnTop = function() {
var hash = window.location.hash;
if ( hash.length < 2 ) { return false; }
var target = document.getElementById( hash.slice(1) );
if ( target === null ) { return false; }
var top = target.getBoundingClientRect().top;
return (top < 2) && (top > -2);
};
// Hide navbar on load if hash target is on top
var navBar = document.getElementById("navbar");
var navBarToggler = document.getElementById("sk-navbar-toggler");
var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
var $window = $(window);
hideNavBar = function() {
navBar.style.top = navBarHeightHidden;
};
showNavBar = function() {
navBar.style.top = "0";
}
if (hashTargetOnTop()) {
hideNavBar()
}
var prevScrollpos = window.pageYOffset;
hideOnScroll = function(lastScrollTop) {
if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
return;
}
if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
hideNavBar()
} else {
showNavBar()
}
prevScrollpos = lastScrollTop;
};
/*** high performance scroll event listener***/
var raf = window.requestAnimationFrame ||
window.webkitRequestAnimationFrame ||
window.mozRequestAnimationFrame ||
window.msRequestAnimationFrame ||
window.oRequestAnimationFrame;
var lastScrollTop = $window.scrollTop();
if (raf) {
loop();
}
function loop() {
var scrollTop = $window.scrollTop();
if (lastScrollTop === scrollTop) {
raf(loop);
return;
} else {
lastScrollTop = scrollTop;
hideOnScroll(lastScrollTop);
raf(loop);
}
}
})();
});
</script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
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</body>
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