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<!DOCTYPE html>
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<title>Algorithms — daal4py 2021.1 documentation</title>
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<p class="caption"><span class="caption-text">Contents:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="index.html">About daal4py</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">Data</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Supported Algorithms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#classification">Classification</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#decision-forest-classification">Decision Forest Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#decision-tree-classification">Decision Tree Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#gradient-boosted-classification">Gradient Boosted Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#k-nearest-neighbors-knn">k-Nearest Neighbors (kNN)</a></li>
<li class="toctree-l3"><a class="reference internal" href="#brute-force-k-nearest-neighbors-knn">Brute-force k-Nearest Neighbors (kNN)</a></li>
<li class="toctree-l3"><a class="reference internal" href="#adaboost-classification">AdaBoost Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#brownboost-classification">BrownBoost Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#logitboost-classification">LogitBoost Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#stump-weak-learner-classification">Stump Weak Learner Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="#multinomial-naive-bayes">Multinomial Naive Bayes</a></li>
<li class="toctree-l3"><a class="reference internal" href="#support-vector-machine-svm">Support Vector Machine (SVM)</a></li>
<li class="toctree-l3"><a class="reference internal" href="#logistic-regression">Logistic Regression</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#regression">Regression</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#decision-forest-regression">Decision Forest Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="#decision-tree-regression">Decision Tree Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="#gradient-boosted-regression">Gradient Boosted Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="#linear-regression">Linear Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="#least-absolute-shrinkage-and-selection-operator">Least Absolute Shrinkage and Selection Operator</a></li>
<li class="toctree-l3"><a class="reference internal" href="#ridge-regression">Ridge Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="#stump-regression">Stump Regression</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#principal-component-analysis-pca">Principal Component Analysis (PCA)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#principal-component-analysis-pca-transform">Principal Component Analysis (PCA) Transform</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#k-means-clustering">K-Means Clustering</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#k-means-initialization">K-Means Initialization</a></li>
<li class="toctree-l3"><a class="reference internal" href="#k-means">K-Means</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#density-based-spatial-clustering-of-applications-with-noise">Density-Based Spatial Clustering of Applications with Noise</a></li>
<li class="toctree-l2"><a class="reference internal" href="#outlier-detection">Outlier Detection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#multivariate-outlier-detection">Multivariate Outlier Detection</a></li>
<li class="toctree-l3"><a class="reference internal" href="#univariate-outlier-detection">Univariate Outlier Detection</a></li>
<li class="toctree-l3"><a class="reference internal" href="#multivariate-bacon-outlier-detection">Multivariate Bacon Outlier Detection</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#optimization-solvers">Optimization Solvers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#objective-functions">Objective Functions</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#mean-squared-error-algorithm-mse">Mean Squared Error Algorithm (MSE)</a></li>
<li class="toctree-l4"><a class="reference internal" href="#logistic-loss">Logistic Loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="#cross-entropy-loss">Cross-entropy Loss</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#iterative-solvers">Iterative Solvers</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#stochastic-gradient-descent-algorithm">Stochastic Gradient Descent Algorithm</a></li>
<li class="toctree-l4"><a class="reference internal" href="#limited-memory-broyden-fletcher-goldfarb-shanno-algorithm">Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm</a></li>
<li class="toctree-l4"><a class="reference internal" href="#adaptive-subgradient-method">Adaptive Subgradient Method</a></li>
<li class="toctree-l4"><a class="reference internal" href="#stochastic-average-gradient-descent">Stochastic Average Gradient Descent</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#distances">Distances</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#cosine-distance-matrix">Cosine Distance Matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="#correlation-distance-matrix">Correlation Distance Matrix</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#expectation-maximization-em">Expectation-Maximization (EM)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#initialization-for-the-gaussian-mixture-model">Initialization for the Gaussian Mixture Model</a></li>
<li class="toctree-l3"><a class="reference internal" href="#em-algorithm-for-the-gaussian-mixture-model">EM algorithm for the Gaussian Mixture Model</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="#qr-decomposition">QR Decomposition</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#qr-decomposition-without-pivoting">QR Decomposition (without pivoting)</a></li>
<li class="toctree-l3"><a class="reference internal" href="#pivoted-qr-decomposition">Pivoted QR Decomposition</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#normalization">Normalization</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#z-score">Z-Score</a></li>
<li class="toctree-l3"><a class="reference internal" href="#min-max">Min-Max</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#random-number-engines">Random Number Engines</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#mt19937">mt19937</a></li>
<li class="toctree-l3"><a class="reference internal" href="#mt2203">mt2203</a></li>
<li class="toctree-l3"><a class="reference internal" href="#mcg59">mcg59</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#distributions">Distributions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#bernoulli">Bernoulli</a></li>
<li class="toctree-l3"><a class="reference internal" href="#normal">Normal</a></li>
<li class="toctree-l3"><a class="reference internal" href="#uniform">Uniform</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#association-rules">Association Rules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#correlation-and-variance-covariance-matrices">Correlation and Variance-Covariance Matrices</a></li>
<li class="toctree-l2"><a class="reference internal" href="#implicit-alternating-least-squares-implicit-als">Implicit Alternating Least Squares (implicit ALS)</a></li>
<li class="toctree-l2"><a class="reference internal" href="#moments-of-low-order">Moments of Low Order</a></li>
<li class="toctree-l2"><a class="reference internal" href="#quantiles">Quantiles</a></li>
<li class="toctree-l2"><a class="reference internal" href="#singular-value-decomposition-svd">Singular Value Decomposition (SVD)</a></li>
<li class="toctree-l2"><a class="reference internal" href="#sorting">Sorting</a></li>
<li class="toctree-l2"><a class="reference internal" href="#trees">Trees</a></li>
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<li class="toctree-l1"><a class="reference internal" href="scaling.html">Distribution</a></li>
<li class="toctree-l1"><a class="reference internal" href="streaming.html">Streaming</a></li>
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<div class="section" id="algorithms">
<h1>Algorithms<a class="headerlink" href="#algorithms" title="Permalink to this headline">¶</a></h1>
<div class="section" id="classification">
<h2>Classification<a class="headerlink" href="#classification" title="Permalink to this headline">¶</a></h2>
<p>See also <a class="reference external" href="https://oneapi-src.github.io/oneDAL/daal/usage/training-and-prediction/classification.html">Intel(R) oneAPI Data Analytics Library Classification</a>.</p>
<div class="section" id="decision-forest-classification">
<h3>Decision Forest Classification<a class="headerlink" href="#decision-forest-classification" title="Permalink to this headline">¶</a></h3>
<p>Parameters and semantics are described in <a class="reference external" href="https://oneapi-src.github.io/oneDAL/daal/algorithms/decision_forest/decision-forest-classification.html">Intel(R) oneAPI Data Analytics Library Classification Decision Forest</a>.</p>
<p class="rubric">Examples:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/decision_forest_classification_batch.py">Single-Process Decision Forest Classification</a></p></li>
</ul>
<dl class="py class">
<dt id="daal4py.decision_forest_classification_training">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_forest_classification_training</code><a class="headerlink" href="#daal4py.decision_forest_classification_training" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nClasses</strong> (<em>size_t</em>) – Number of classes</p></li>
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for Decision forest, double or float</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] Decision forest computation method</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
<li><p><strong>nTrees</strong> (<em>size_t</em>) – [optional, default: -1] Number of trees in the forest. Default is 10</p></li>
<li><p><strong>observationsPerTreeFraction</strong> (<em>double</em>) – [optional, default: get_nan64()] Fraction of observations used for a training of one tree, 0 to 1. Default is 1 (sampling with replacement)</p></li>
<li><p><strong>featuresPerNode</strong> (<em>size_t</em>) – [optional, default: -1] Number of features tried as possible splits per node. If 0 then sqrt(p) for classification, p/3 for regression, where p is the total number of features.</p></li>
<li><p><strong>maxTreeDepth</strong> (<em>size_t</em>) – [optional, default: -1] Maximal tree depth. Default is 0 (unlimited)</p></li>
<li><p><strong>minObservationsInLeafNode</strong> (<em>size_t</em>) – [optional, default: -1] Minimal number of observations in a leaf node. Default is 1 for classification, 5 for regression.</p></li>
<li><p><strong>engine</strong> (<em>engines_batchbase__iface__</em>) – [optional, default: None] Engine for the random numbers generator used by the algorithms</p></li>
<li><p><strong>impurityThreshold</strong> (<em>double</em>) – [optional, default: get_nan64()] Threshold value used as stopping criteria: if the impurity value in the node is smaller than the threshold then the node is not split anymore.</p></li>
<li><p><strong>varImportance</strong> (<em>str</em>) – [optional, default: “”] Variable importance computation mode</p></li>
<li><p><strong>resultsToCompute</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
<li><p><strong>memorySavingMode</strong> (<em>bool</em>) – [optional, default: False] If true then use memory saving (but slower) mode</p></li>
<li><p><strong>bootstrap</strong> (<em>bool</em>) – [optional, default: False] If true then training set for a tree is a bootstrap of the whole training set</p></li>
<li><p><strong>minObservationsInSplitNode</strong> (<em>size_t</em>) – [optional, default: -1] Minimal number of observations in a split node. Default 2</p></li>
<li><p><strong>minWeightFractionInLeafNode</strong> (<em>double</em>) – [optional, default: get_nan64()] The minimum weighted fraction of the sum total of weights (of all the input observations) required to be at a leaf node, 0.0 to 0.5. Default is 0.0</p></li>
<li><p><strong>minImpurityDecreaseInSplitNode</strong> (<em>double</em>) – [optional, default: get_nan64()] A node will be split if this split induces a decrease of the impurity greater than or equal to the value, non-negative. Default is 0.0</p></li>
<li><p><strong>maxLeafNodes</strong> (<em>size_t</em>) – [optional, default: -1] Maximum number of leaf node. Default is 0 (unlimited)</p></li>
<li><p><strong>maxBins</strong> (<em>size_t</em>) – [optional, default: -1] Used with ‘hist’ split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs</p></li>
<li><p><strong>minBinSize</strong> (<em>size_t</em>) – [optional, default: -1] Used with ‘hist’ split finding method only. Minimal number of observations in a bin. Default is 5</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.decision_forest_classification_training.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">weights</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.decision_forest_classification_training.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Training data set</p></li>
<li><p><strong>labels</strong> (<em>data_or_file</em>) – Labels of the training data set</p></li>
<li><p><strong>weights</strong> (<em>data_or_file</em>) – [optional, default: None] Optional. Weights of the observations in the training data set</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#daal4py.decision_forest_classification_training_result" title="daal4py.decision_forest_classification_training_result">decision_forest_classification_training_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.decision_forest_classification_training_result">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_forest_classification_training_result</code><a class="headerlink" href="#daal4py.decision_forest_classification_training_result" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_training_result.model">
<code class="sig-name descname">model</code><a class="headerlink" href="#daal4py.decision_forest_classification_training_result.model" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#daal4py.decision_forest_classification_model" title="daal4py.decision_forest_classification_model">decision_forest_classification_model</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_training_result.outOfBagError">
<code class="sig-name descname">outOfBagError</code><a class="headerlink" href="#daal4py.decision_forest_classification_training_result.outOfBagError" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_training_result.outOfBagErrorPerObservation">
<code class="sig-name descname">outOfBagErrorPerObservation</code><a class="headerlink" href="#daal4py.decision_forest_classification_training_result.outOfBagErrorPerObservation" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_training_result.variableImportance">
<code class="sig-name descname">variableImportance</code><a class="headerlink" href="#daal4py.decision_forest_classification_training_result.variableImportance" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.decision_forest_classification_prediction">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_forest_classification_prediction</code><a class="headerlink" href="#daal4py.decision_forest_classification_prediction" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nClasses</strong> (<em>size_t</em>) – Number of classes</p></li>
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for the decision_forest algortithm, double or float</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] decision_forest computation method</p></li>
<li><p><strong>votingMethod</strong> (<em>str</em>) – [optional, default: “”]</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.decision_forest_classification_prediction.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.decision_forest_classification_prediction.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Input data set</p></li>
<li><p><strong>model</strong> (<em>decision_forest_classification_modelptr</em>) – Input model trained by the classification algorithm</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#id40" title="daal4py.classifier_prediction_result">classifier_prediction_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.classifier_prediction_result">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">classifier_prediction_result</code><a class="headerlink" href="#daal4py.classifier_prediction_result" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.classifier_prediction_result.logProbabilities">
<code class="sig-name descname">logProbabilities</code><a class="headerlink" href="#daal4py.classifier_prediction_result.logProbabilities" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.classifier_prediction_result.prediction">
<code class="sig-name descname">prediction</code><a class="headerlink" href="#daal4py.classifier_prediction_result.prediction" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.classifier_prediction_result.probabilities">
<code class="sig-name descname">probabilities</code><a class="headerlink" href="#daal4py.classifier_prediction_result.probabilities" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.decision_forest_classification_model">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_forest_classification_model</code><a class="headerlink" href="#daal4py.decision_forest_classification_model" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_model.NFeatures">
<code class="sig-name descname">NFeatures</code><a class="headerlink" href="#daal4py.decision_forest_classification_model.NFeatures" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_model.NumberOfClasses">
<code class="sig-name descname">NumberOfClasses</code><a class="headerlink" href="#daal4py.decision_forest_classification_model.NumberOfClasses" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_model.NumberOfFeatures">
<code class="sig-name descname">NumberOfFeatures</code><a class="headerlink" href="#daal4py.decision_forest_classification_model.NumberOfFeatures" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_forest_classification_model.NumberOfTrees">
<code class="sig-name descname">NumberOfTrees</code><a class="headerlink" href="#daal4py.decision_forest_classification_model.NumberOfTrees" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="decision-tree-classification">
<h3>Decision Tree Classification<a class="headerlink" href="#decision-tree-classification" title="Permalink to this headline">¶</a></h3>
<p>Parameters and semantics are described in <a class="reference external" href="https://oneapi-src.github.io/oneDAL/daal/algorithms/decision_tree/decision-tree-classification.html">Intel(R) oneAPI Data Analytics Library Classification Decision Tree</a>.</p>
<p class="rubric">Examples:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/decision_tree_classification_batch.py">Single-Process Decision Tree Classification</a></p></li>
</ul>
<dl class="py class">
<dt id="daal4py.decision_tree_classification_training">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_tree_classification_training</code><a class="headerlink" href="#daal4py.decision_tree_classification_training" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nClasses</strong> (<em>size_t</em>) – Number of classes</p></li>
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for Decision tree model-based training, double or float</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] Decision tree training method</p></li>
<li><p><strong>pruning</strong> (<em>str</em>) – [optional, default: “”] Pruning method for Decision tree</p></li>
<li><p><strong>maxTreeDepth</strong> (<em>size_t</em>) – [optional, default: -1] Maximum tree depth. 0 means unlimited depth.</p></li>
<li><p><strong>minObservationsInLeafNodes</strong> (<em>size_t</em>) – [optional, default: -1] Minimum number of observations in the leaf node. Can be any positive number.</p></li>
<li><p><strong>nBins</strong> (<em>size_t</em>) – [optional, default: -1] The number of bins used to compute probabilities of the observations belonging to the class. The only supported value for current version of the library is 1.</p></li>
<li><p><strong>splitCriterion</strong> (<em>str</em>) – [optional, default: “”] Split criterion for Decision tree classification</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.decision_tree_classification_training.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">dataForPruning</span></em>, <em class="sig-param"><span class="n">labelsForPruning</span></em>, <em class="sig-param"><span class="n">weights</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.decision_tree_classification_training.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Training data set</p></li>
<li><p><strong>labels</strong> (<em>data_or_file</em>) – Labels of the training data set</p></li>
<li><p><strong>dataForPruning</strong> (<em>data_or_file</em>) – Pruning data set</p></li>
<li><p><strong>labelsForPruning</strong> (<em>data_or_file</em>) – Labels of the pruning data set</p></li>
<li><p><strong>weights</strong> (<em>data_or_file</em>) – [optional, default: None] Optional. Weights of the observations in the training data set</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#daal4py.decision_tree_classification_training_result" title="daal4py.decision_tree_classification_training_result">decision_tree_classification_training_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.decision_tree_classification_training_result">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_tree_classification_training_result</code><a class="headerlink" href="#daal4py.decision_tree_classification_training_result" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.decision_tree_classification_training_result.model">
<code class="sig-name descname">model</code><a class="headerlink" href="#daal4py.decision_tree_classification_training_result.model" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#daal4py.decision_tree_classification_model" title="daal4py.decision_tree_classification_model">decision_tree_classification_model</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.decision_tree_classification_prediction">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_tree_classification_prediction</code><a class="headerlink" href="#daal4py.decision_tree_classification_prediction" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for Decision tree model-based prediction</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] Computation method in the batch processing mode</p></li>
<li><p><strong>pruning</strong> (<em>str</em>) – [optional, default: “”] Pruning method for Decision tree</p></li>
<li><p><strong>maxTreeDepth</strong> (<em>size_t</em>) – [optional, default: -1] Maximum tree depth. 0 means unlimited depth.</p></li>
<li><p><strong>minObservationsInLeafNodes</strong> (<em>size_t</em>) – [optional, default: -1] Minimum number of observations in the leaf node. Can be any positive number.</p></li>
<li><p><strong>nBins</strong> (<em>size_t</em>) – [optional, default: -1] The number of bins used to compute probabilities of the observations belonging to the class. The only supported value for current version of the library is 1.</p></li>
<li><p><strong>splitCriterion</strong> (<em>str</em>) – [optional, default: “”] Split criterion for Decision tree classification</p></li>
<li><p><strong>nClasses</strong> (<em>size_t</em>) – [optional, default: -1] Number of classes</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.decision_tree_classification_prediction.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.decision_tree_classification_prediction.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Input data set</p></li>
<li><p><strong>model</strong> (<em>decision_tree_classification_modelptr</em>) – Input model trained by the classification algorithm</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#id40" title="daal4py.classifier_prediction_result">classifier_prediction_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="id0">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">classifier_prediction_result</code><a class="headerlink" href="#id0" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="id1">
<code class="sig-name descname">logProbabilities</code><a class="headerlink" href="#id1" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="id2">
<code class="sig-name descname">prediction</code><a class="headerlink" href="#id2" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="id3">
<code class="sig-name descname">probabilities</code><a class="headerlink" href="#id3" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.decision_tree_classification_model">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">decision_tree_classification_model</code><a class="headerlink" href="#daal4py.decision_tree_classification_model" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.decision_tree_classification_model.NFeatures">
<code class="sig-name descname">NFeatures</code><a class="headerlink" href="#daal4py.decision_tree_classification_model.NFeatures" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.decision_tree_classification_model.NumberOfFeatures">
<code class="sig-name descname">NumberOfFeatures</code><a class="headerlink" href="#daal4py.decision_tree_classification_model.NumberOfFeatures" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="gradient-boosted-classification">
<h3>Gradient Boosted Classification<a class="headerlink" href="#gradient-boosted-classification" title="Permalink to this headline">¶</a></h3>
<p>Parameters and semantics are described in <a class="reference external" href="https://oneapi-src.github.io/oneDAL/daal/algorithms/gradient_boosted_trees/gradient-boosted-trees-classification.html">Intel(R) oneAPI Data Analytics Library Classification Gradient Boosted Tree</a>.</p>
<p class="rubric">Examples:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/gradient_boosted_classification_batch.py">Single-Process Gradient Boosted Classification</a></p></li>
</ul>
<dl class="py class">
<dt id="daal4py.gbt_classification_training">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">gbt_classification_training</code><a class="headerlink" href="#daal4py.gbt_classification_training" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nClasses</strong> (<em>size_t</em>) – Number of classes</p></li>
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for Gradient Boosted Trees, double or float</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] Gradient Boosted Trees computation method</p></li>
<li><p><strong>loss</strong> (<em>str</em>) – [optional, default: “”] Loss function type</p></li>
<li><p><strong>varImportance</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag VariableImportanceModes that indicates the variable importance computation modes</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
<li><p><strong>splitMethod</strong> (<em>str</em>) – [optional, default: “”] Split finding method. Default is exact</p></li>
<li><p><strong>maxIterations</strong> (<em>size_t</em>) – [optional, default: -1] Maximal number of iterations of the gradient boosted trees training algorithm. Default is 50</p></li>
<li><p><strong>maxTreeDepth</strong> (<em>size_t</em>) – [optional, default: -1] Maximal tree depth, 0 for unlimited. Default is 6</p></li>
<li><p><strong>shrinkage</strong> (<em>double</em>) – [optional, default: get_nan64()] Learning rate of the boosting procedure. Scales the contribution of each tree by a factor (0, 1]. Default is 0.3</p></li>
<li><p><strong>minSplitLoss</strong> (<em>double</em>) – [optional, default: get_nan64()] Loss regularization parameter. Min loss reduction required to make a further partition on a leaf node of the tree. Range: [0, inf). Default is 0</p></li>
<li><p><strong>lambda</strong> (<em>double</em>) – [optional, default: get_nan64()] L2 regularization parameter on weights. Range: [0, inf). Default is 1</p></li>
<li><p><strong>observationsPerTreeFraction</strong> (<em>double</em>) – [optional, default: get_nan64()] Fraction of observations used for a training of one tree, sampling without replacement. Range: (0, 1]. Default is 1 (no sampling, entire dataset is used)</p></li>
<li><p><strong>featuresPerNode</strong> (<em>size_t</em>) – [optional, default: -1] Number of features tried as possible splits per node. Range : [0, p] where p is the total number of features. Default is 0 (use all features)</p></li>
<li><p><strong>minObservationsInLeafNode</strong> (<em>size_t</em>) – [optional, default: -1] Minimal number of observations in a leaf node. Default is 5.</p></li>
<li><p><strong>memorySavingMode</strong> (<em>bool</em>) – [optional, default: False] If true then use memory saving (but slower) mode. Default is false</p></li>
<li><p><strong>engine</strong> (<em>engines_batchbase__iface__</em>) – [optional, default: None] Engine for the random numbers generator used by the algorithms</p></li>
<li><p><strong>maxBins</strong> (<em>size_t</em>) – [optional, default: -1] Used with ‘inexact’ split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs</p></li>
<li><p><strong>minBinSize</strong> (<em>size_t</em>) – [optional, default: -1] Used with ‘inexact’ split finding method only. Minimal number of observations in a bin. Default is 5</p></li>
<li><p><strong>internalOptions</strong> (<em>int</em>) – [optional, default: -1] Internal options</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.gbt_classification_training.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">weights</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.gbt_classification_training.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Training data set</p></li>
<li><p><strong>labels</strong> (<em>data_or_file</em>) – Labels of the training data set</p></li>
<li><p><strong>weights</strong> (<em>data_or_file</em>) – [optional, default: None] Optional. Weights of the observations in the training data set</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#daal4py.gbt_classification_training_result" title="daal4py.gbt_classification_training_result">gbt_classification_training_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.gbt_classification_training_result">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">gbt_classification_training_result</code><a class="headerlink" href="#daal4py.gbt_classification_training_result" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_training_result.model">
<code class="sig-name descname">model</code><a class="headerlink" href="#daal4py.gbt_classification_training_result.model" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#daal4py.gbt_classification_model" title="daal4py.gbt_classification_model">gbt_classification_model</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_training_result.variableImportanceByCover">
<code class="sig-name descname">variableImportanceByCover</code><a class="headerlink" href="#daal4py.gbt_classification_training_result.variableImportanceByCover" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_training_result.variableImportanceByGain">
<code class="sig-name descname">variableImportanceByGain</code><a class="headerlink" href="#daal4py.gbt_classification_training_result.variableImportanceByGain" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_training_result.variableImportanceByTotalCover">
<code class="sig-name descname">variableImportanceByTotalCover</code><a class="headerlink" href="#daal4py.gbt_classification_training_result.variableImportanceByTotalCover" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_training_result.variableImportanceByTotalGain">
<code class="sig-name descname">variableImportanceByTotalGain</code><a class="headerlink" href="#daal4py.gbt_classification_training_result.variableImportanceByTotalGain" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_training_result.variableImportanceByWeight">
<code class="sig-name descname">variableImportanceByWeight</code><a class="headerlink" href="#daal4py.gbt_classification_training_result.variableImportanceByWeight" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.gbt_classification_prediction">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">gbt_classification_prediction</code><a class="headerlink" href="#daal4py.gbt_classification_prediction" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nClasses</strong> (<em>size_t</em>) – Number of classes</p></li>
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for the gbt algortithm, double or float</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] gradient boosted trees computation method</p></li>
<li><p><strong>nIterations</strong> (<em>size_t</em>) – [optional, default: -1] Number of iterations of the trained model to be used for prediction</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.gbt_classification_prediction.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.gbt_classification_prediction.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Input data set</p></li>
<li><p><strong>model</strong> (<em>gbt_classification_modelptr</em>) – Input model trained by the classification algorithm</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#id40" title="daal4py.classifier_prediction_result">classifier_prediction_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="id4">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">classifier_prediction_result</code><a class="headerlink" href="#id4" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="id5">
<code class="sig-name descname">logProbabilities</code><a class="headerlink" href="#id5" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="id6">
<code class="sig-name descname">prediction</code><a class="headerlink" href="#id6" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="id7">
<code class="sig-name descname">probabilities</code><a class="headerlink" href="#id7" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.gbt_classification_model">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">gbt_classification_model</code><a class="headerlink" href="#daal4py.gbt_classification_model" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_model.NFeatures">
<code class="sig-name descname">NFeatures</code><a class="headerlink" href="#daal4py.gbt_classification_model.NFeatures" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_model.NumberOfFeatures">
<code class="sig-name descname">NumberOfFeatures</code><a class="headerlink" href="#daal4py.gbt_classification_model.NumberOfFeatures" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="daal4py.gbt_classification_model.NumberOfTrees">
<code class="sig-name descname">NumberOfTrees</code><a class="headerlink" href="#daal4py.gbt_classification_model.NumberOfTrees" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>size_t</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="k-nearest-neighbors-knn">
<h3>k-Nearest Neighbors (kNN)<a class="headerlink" href="#k-nearest-neighbors-knn" title="Permalink to this headline">¶</a></h3>
<p>Parameters and semantics are described in <a class="reference external" href="https://oneapi-src.github.io/oneDAL/daal/algorithms/k_nearest_neighbors/k-nearest-neighbors-knn-classifier.html">Intel(R) oneAPI Data Analytics Library k-Nearest Neighbors (kNN)</a>.</p>
<p class="rubric">Examples:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/master/examples/daal4py/kdtree_knn_classification_batch.py">Single-Process kNN</a></p></li>
</ul>
<dl class="py class">
<dt id="daal4py.kdtree_knn_classification_training">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">kdtree_knn_classification_training</code><a class="headerlink" href="#daal4py.kdtree_knn_classification_training" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for KD-tree based kNN model-based training, double or float</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] KD-tree based kNN training method</p></li>
<li><p><strong>k</strong> (<em>size_t</em>) – [optional, default: -1] Number of neighbors</p></li>
<li><p><strong>dataUseInModel</strong> (<em>str</em>) – [optional, default: “”] The option to enable/disable an usage of the input dataset in kNN model</p></li>
<li><p><strong>engine</strong> (<em>engines_batchbase__iface__</em>) – [optional, default: None] Engine for random choosing elements from training dataset</p></li>
<li><p><strong>resultsToCompute</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
<li><p><strong>voteWeights</strong> (<em>str</em>) – [optional, default: “”] Weight function used in prediction</p></li>
<li><p><strong>nClasses</strong> (<em>size_t</em>) – [optional, default: -1] Number of classes</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.kdtree_knn_classification_training.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">weights</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.kdtree_knn_classification_training.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Training data set</p></li>
<li><p><strong>labels</strong> (<em>data_or_file</em>) – [optional, default: None] Labels of the training data set</p></li>
<li><p><strong>weights</strong> (<em>data_or_file</em>) – [optional, default: None] Optional. Weights of the observations in the training data set</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#daal4py.kdtree_knn_classification_training_result" title="daal4py.kdtree_knn_classification_training_result">kdtree_knn_classification_training_result</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.kdtree_knn_classification_training_result">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">kdtree_knn_classification_training_result</code><a class="headerlink" href="#daal4py.kdtree_knn_classification_training_result" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="daal4py.kdtree_knn_classification_training_result.model">
<code class="sig-name descname">model</code><a class="headerlink" href="#daal4py.kdtree_knn_classification_training_result.model" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#daal4py.kdtree_knn_classification_model" title="daal4py.kdtree_knn_classification_model">kdtree_knn_classification_model</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="daal4py.kdtree_knn_classification_prediction">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">kdtree_knn_classification_prediction</code><a class="headerlink" href="#daal4py.kdtree_knn_classification_prediction" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fptype</strong> (<em>str</em>) – [optional, default: “double”] Data type to use in intermediate computations for KD-tree based kNN model-based prediction</p></li>
<li><p><strong>method</strong> (<em>str</em>) – [optional, default: “defaultDense”] Computation method in the batch processing mode</p></li>
<li><p><strong>k</strong> (<em>size_t</em>) – [optional, default: -1] Number of neighbors</p></li>
<li><p><strong>dataUseInModel</strong> (<em>str</em>) – [optional, default: “”] The option to enable/disable an usage of the input dataset in kNN model</p></li>
<li><p><strong>engine</strong> (<em>engines_batchbase__iface__</em>) – [optional, default: None] Engine for random choosing elements from training dataset</p></li>
<li><p><strong>resultsToCompute</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
<li><p><strong>voteWeights</strong> (<em>str</em>) – [optional, default: “”] Weight function used in prediction</p></li>
<li><p><strong>nClasses</strong> (<em>size_t</em>) – [optional, default: -1] Number of classes</p></li>
<li><p><strong>resultsToEvaluate</strong> (<em>str</em>) – [optional, default: “”] 64 bit integer flag that indicates the results to compute</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="daal4py.kdtree_knn_classification_prediction.compute">
<code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">model</span></em><span class="sig-paren">)</span><a class="headerlink" href="#daal4py.kdtree_knn_classification_prediction.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Do the actual computation on provided input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>data_or_file</em>) – Input data set</p></li>
<li><p><strong>model</strong> (<em>kdtree_knn_classification_modelptr</em>) – Input model trained by the classification algorithm</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>kdtree_knn_classification_prediction_result</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt id="id8">
<em class="property">class </em><code class="sig-prename descclassname">daal4py.</code><code class="sig-name descname">classifier_prediction_result</code><a class="headerlink" href="#id8" title="Permalink to this definition">¶</a></dt>
<dd><p>Properties:</p>
<dl class="py attribute">
<dt id="id9">
<code class="sig-name descname">logProbabilities</code><a class="headerlink" href="#id9" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt id="id10">
<code class="sig-name descname">prediction</code><a class="headerlink" href="#id10" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Type</dt>
<dd class="field-odd"><p>Numpy array</p>