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<h1 title="Uses of Package org.apache.ignite.ml.preprocessing" class="title">Uses of Package<br>org.apache.ignite.ml.preprocessing</h1>
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<table class="useSummary" border="0" cellpadding="3" cellspacing="0" summary="Use table, listing packages, and an explanation">
<caption><span>Packages that use <a href="../../../../../org/apache/ignite/ml/preprocessing/package-summary.html">org.apache.ignite.ml.preprocessing</a></span><span class="tabEnd">&nbsp;</span></caption>
<tr>
<th class="colFirst" scope="col">Package</th>
<th class="colLast" scope="col">Description</th>
</tr>
<tbody>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.clustering.gmm">org.apache.ignite.ml.clustering.gmm</a></td>
<td class="colLast">
<div class="block">Contains Gauss Mixture Model clustering algorithm (see <a href="../../../../../org/apache/ignite/ml/clustering/gmm/GmmModel.html" title="class in org.apache.ignite.ml.clustering.gmm"><code>GmmModel</code></a>).</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.clustering.kmeans">org.apache.ignite.ml.clustering.kmeans</a></td>
<td class="colLast">
<div class="block">Contains kMeans clustering algorithm.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.bagging">org.apache.ignite.ml.composition.bagging</a></td>
<td class="colLast">&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.boosting">org.apache.ignite.ml.composition.boosting</a></td>
<td class="colLast">
<div class="block">Contains Gradient Boosting regression and classification abstract classes
allowing regressor type selecting in child classes.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.boosting.convergence">org.apache.ignite.ml.composition.boosting.convergence</a></td>
<td class="colLast">
<div class="block">Package contains implementation of convergence checking algorithms for gradient boosting.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.boosting.convergence.mean">org.apache.ignite.ml.composition.boosting.convergence.mean</a></td>
<td class="colLast">
<div class="block">Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.boosting.convergence.median">org.apache.ignite.ml.composition.boosting.convergence.median</a></td>
<td class="colLast">
<div class="block">Contains implementation of convergence checking computer by median of medians of errors in dataset.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.boosting.convergence.simple">org.apache.ignite.ml.composition.boosting.convergence.simple</a></td>
<td class="colLast">
<div class="block">Contains implementation of Stub for convergence checking.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.combinators.parallel">org.apache.ignite.ml.composition.combinators.parallel</a></td>
<td class="colLast">
<div class="block">Contains parallel combiners (ensembles) of trainers and models.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.combinators.sequential">org.apache.ignite.ml.composition.combinators.sequential</a></td>
<td class="colLast">
<div class="block">Contains sequential combinators of trainers and models.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.composition.stacking">org.apache.ignite.ml.composition.stacking</a></td>
<td class="colLast">
<div class="block">Contains classes used for training with stacking technique.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.dataset">org.apache.ignite.ml.dataset</a></td>
<td class="colLast">
<div class="block">Base package for machine learning dataset classes.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.dataset.feature.extractor">org.apache.ignite.ml.dataset.feature.extractor</a></td>
<td class="colLast">
<div class="block">Package for upstream object vectorizations.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.dataset.feature.extractor.impl">org.apache.ignite.ml.dataset.feature.extractor.impl</a></td>
<td class="colLast">
<div class="block">Package contains default implementations of <a href="../../../../../org/apache/ignite/ml/dataset/feature/extractor/Vectorizer.html" title="class in org.apache.ignite.ml.dataset.feature.extractor"><code>Vectorizer</code></a>.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.dataset.impl.bootstrapping">org.apache.ignite.ml.dataset.impl.bootstrapping</a></td>
<td class="colLast">
<div class="block">Base package for bootstrapped implementation of machine learning dataset.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.dataset.primitive">org.apache.ignite.ml.dataset.primitive</a></td>
<td class="colLast">
<div class="block">Package that contains basic primitives build on top of <a href="../../../../../org/apache/ignite/ml/dataset/Dataset.html" title="interface in org.apache.ignite.ml.dataset"><code>Dataset</code></a>.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.dataset.primitive.builder.data">org.apache.ignite.ml.dataset.primitive.builder.data</a></td>
<td class="colLast">
<div class="block">Contains partition <code>data</code> builders.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.knn">org.apache.ignite.ml.knn</a></td>
<td class="colLast">
<div class="block">Contains main APIs for kNN algorithms.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.knn.ann">org.apache.ignite.ml.knn.ann</a></td>
<td class="colLast">
<div class="block">Contains main APIs for ANN classification algorithms.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.knn.utils">org.apache.ignite.ml.knn.utils</a></td>
<td class="colLast">
<div class="block">Contains util functionality for kNN algorithms.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.multiclass">org.apache.ignite.ml.multiclass</a></td>
<td class="colLast">
<div class="block">Contains various multi-classifier models and trainers.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.naivebayes.compound">org.apache.ignite.ml.naivebayes.compound</a></td>
<td class="colLast">
<div class="block">Contains Compound naive Bayes classifier.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.naivebayes.discrete">org.apache.ignite.ml.naivebayes.discrete</a></td>
<td class="colLast">
<div class="block">Contains Bernoulli naive Bayes classifier.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.naivebayes.gaussian">org.apache.ignite.ml.naivebayes.gaussian</a></td>
<td class="colLast">
<div class="block">Contains Gaussian naive Bayes classifier.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.nn">org.apache.ignite.ml.nn</a></td>
<td class="colLast">
<div class="block">Contains neural networks and related classes.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.pipeline">org.apache.ignite.ml.pipeline</a></td>
<td class="colLast">
<div class="block">Contains Pipeline API.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing">org.apache.ignite.ml.preprocessing</a></td>
<td class="colLast">
<div class="block">Base package for machine learning preprocessing classes.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.binarization">org.apache.ignite.ml.preprocessing.binarization</a></td>
<td class="colLast">
<div class="block">Contains binarization preprocessor.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.developer">org.apache.ignite.ml.preprocessing.developer</a></td>
<td class="colLast">
<div class="block">Contains Developer API preprocessors.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.encoding">org.apache.ignite.ml.preprocessing.encoding</a></td>
<td class="colLast">
<div class="block">Contains encoding preprocessors.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.encoding.frequency">org.apache.ignite.ml.preprocessing.encoding.frequency</a></td>
<td class="colLast">
<div class="block">Contains frequency encoding preprocessor.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.encoding.label">org.apache.ignite.ml.preprocessing.encoding.label</a></td>
<td class="colLast">
<div class="block">Contains label encoding preprocessor.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.encoding.onehotencoder">org.apache.ignite.ml.preprocessing.encoding.onehotencoder</a></td>
<td class="colLast">
<div class="block">Contains one hot encoding preprocessor.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.encoding.stringencoder">org.apache.ignite.ml.preprocessing.encoding.stringencoder</a></td>
<td class="colLast">
<div class="block">Contains string encoding preprocessor.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.encoding.target">org.apache.ignite.ml.preprocessing.encoding.target</a></td>
<td class="colLast">
<div class="block">Contains frequency encoding preprocessor.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.finalization">org.apache.ignite.ml.preprocessing.finalization</a></td>
<td class="colLast">
<div class="block">Contains vector finalization preprocessor.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.imputing">org.apache.ignite.ml.preprocessing.imputing</a></td>
<td class="colLast">
<div class="block">Contains Imputer preprocessor.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.maxabsscaling">org.apache.ignite.ml.preprocessing.maxabsscaling</a></td>
<td class="colLast">
<div class="block">Contains Max Abs Scaler preprocessor.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.minmaxscaling">org.apache.ignite.ml.preprocessing.minmaxscaling</a></td>
<td class="colLast">
<div class="block">Contains Min Max Scaler preprocessor.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.normalization">org.apache.ignite.ml.preprocessing.normalization</a></td>
<td class="colLast">
<div class="block">Contains Normalizer preprocessor.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.preprocessing.standardscaling">org.apache.ignite.ml.preprocessing.standardscaling</a></td>
<td class="colLast">
<div class="block">Contains Standard scaler preprocessor.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.regressions.linear">org.apache.ignite.ml.regressions.linear</a></td>
<td class="colLast">
<div class="block">Contains various linear regressions.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.regressions.logistic">org.apache.ignite.ml.regressions.logistic</a></td>
<td class="colLast">
<div class="block">Contains various logistic regressions.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.selection.cv">org.apache.ignite.ml.selection.cv</a></td>
<td class="colLast">
<div class="block">Root package for cross-validation algorithms.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.selection.scoring.cursor">org.apache.ignite.ml.selection.scoring.cursor</a></td>
<td class="colLast">
<div class="block">Util classes used for score calculation.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.selection.scoring.evaluator">org.apache.ignite.ml.selection.scoring.evaluator</a></td>
<td class="colLast">
<div class="block">Package for model evaluator classes.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.structures.partition">org.apache.ignite.ml.structures.partition</a></td>
<td class="colLast">
<div class="block">Contains internal APIs for dataset partitioned labeled datasets.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.svm">org.apache.ignite.ml.svm</a></td>
<td class="colLast">
<div class="block">Contains main APIs for SVM(support vector machines) algorithms.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.trainers">org.apache.ignite.ml.trainers</a></td>
<td class="colLast">
<div class="block">Contains model trainers.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.tree">org.apache.ignite.ml.tree</a></td>
<td class="colLast">
<div class="block">Root package for decision trees.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.tree.boosting">org.apache.ignite.ml.tree.boosting</a></td>
<td class="colLast">
<div class="block">Contains implementation of gradient boosting on trees.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.tree.data">org.apache.ignite.ml.tree.data</a></td>
<td class="colLast">
<div class="block">Contains data and data builder required for decision tree trainers built on top of partition based dataset.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><a href="#org.apache.ignite.ml.tree.randomforest">org.apache.ignite.ml.tree.randomforest</a></td>
<td class="colLast">
<div class="block">Contains random forest implementation classes.</div>
</td>
</tr>
</tbody>
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<th class="colOne" scope="col">Class and Description</th>
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<td class="colOne"><a href="../../../../../org/apache/ignite/ml/preprocessing/class-use/Preprocessor.html#org.apache.ignite.ml.clustering.gmm">Preprocessor</a>
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<td class="colOne"><a href="../../../../../org/apache/ignite/ml/preprocessing/class-use/Preprocessor.html#org.apache.ignite.ml.clustering.kmeans">Preprocessor</a>
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<td class="colOne"><a href="../../../../../org/apache/ignite/ml/preprocessing/class-use/Preprocessor.html#org.apache.ignite.ml.composition.bagging">Preprocessor</a>
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<td class="colOne"><a href="../../../../../org/apache/ignite/ml/preprocessing/class-use/Preprocessor.html#org.apache.ignite.ml.composition.boosting">Preprocessor</a>
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