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| <div class="section" id="logisticregressionmodel"> |
| <h1>LogisticRegressionModel<a class="headerlink" href="#logisticregressionmodel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.classification.</code><code class="sig-name descname">LogisticRegressionModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">weights</span><span class="p">:</span> <span class="n"><a class="reference internal" href="pyspark.mllib.linalg.Vector.html#pyspark.mllib.linalg.Vector" title="pyspark.mllib.linalg.Vector">pyspark.mllib.linalg.Vector</a></span></em>, <em class="sig-param"><span class="n">intercept</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">numFeatures</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">numClasses</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#LogisticRegressionModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Classification model trained using Multinomial/Binary Logistic |
| Regression.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>weights</strong><span class="classifier"><a class="reference internal" href="pyspark.mllib.linalg.Vector.html#pyspark.mllib.linalg.Vector" title="pyspark.mllib.linalg.Vector"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.mllib.linalg.Vector</span></code></a></span></dt><dd><p>Weights computed for every feature.</p> |
| </dd> |
| <dt><strong>intercept</strong><span class="classifier">float</span></dt><dd><p>Intercept computed for this model. (Only used in Binary Logistic |
| Regression. In Multinomial Logistic Regression, the intercepts will |
| not be a single value, so the intercepts will be part of the |
| weights.)</p> |
| </dd> |
| <dt><strong>numFeatures</strong><span class="classifier">int</span></dt><dd><p>The dimension of the features.</p> |
| </dd> |
| <dt><strong>numClasses</strong><span class="classifier">int</span></dt><dd><p>The number of possible outcomes for k classes classification problem |
| in Multinomial Logistic Regression. By default, it is binary |
| logistic regression so numClasses will be set to 2.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">SparseVector</span> |
| <span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]),</span> |
| <span class="gp">... </span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">lrm</span> <span class="o">=</span> <span class="n">LogisticRegressionWithSGD</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span> |
| <span class="go">1</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span> |
| <span class="go">0</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]]))</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="go">[1, 0]</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">clearThreshold</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span> |
| <span class="go">0.279...</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sparse_data</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})),</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})),</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})),</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}))</span> |
| <span class="gp">... </span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">lrm</span> <span class="o">=</span> <span class="n">LogisticRegressionWithSGD</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">sparse_data</span><span class="p">),</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]))</span> |
| <span class="go">1</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]))</span> |
| <span class="go">0</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span> |
| <span class="go">1</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span> |
| <span class="go">0</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">tempfile</span> |
| <span class="gp">>>> </span><span class="n">path</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">lrm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span> <span class="o">=</span> <span class="n">LogisticRegressionModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]))</span> |
| <span class="go">1</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}))</span> |
| <span class="go">0</span> |
| <span class="gp">>>> </span><span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">rmtree</span> |
| <span class="gp">>>> </span><span class="k">try</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">rmtree</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="gp">... </span><span class="k">except</span> <span class="ne">BaseException</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="k">pass</span> |
| <span class="gp">>>> </span><span class="n">multi_class_data</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span> |
| <span class="gp">... </span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">multi_class_data</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">mcm</span> <span class="o">=</span> <span class="n">LogisticRegressionWithLBFGS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">numClasses</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">mcm</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span> |
| <span class="go">0</span> |
| <span class="gp">>>> </span><span class="n">mcm</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span> |
| <span class="go">1</span> |
| <span class="gp">>>> </span><span class="n">mcm</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> |
| <span class="go">2</span> |
| </pre></div> |
| </div> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.clearThreshold" title="pyspark.mllib.classification.LogisticRegressionModel.clearThreshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clearThreshold</span></code></a>()</p></td> |
| <td><p>Clears the threshold so that <cite>predict</cite> will output raw prediction scores.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.load" title="pyspark.mllib.classification.LogisticRegressionModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(sc, path)</p></td> |
| <td><p>Load a model from the given path.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.predict" title="pyspark.mllib.classification.LogisticRegressionModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(x)</p></td> |
| <td><p>Predict values for a single data point or an RDD of points using the model trained.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.save" title="pyspark.mllib.classification.LogisticRegressionModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(sc, path)</p></td> |
| <td><p>Save this model to the given path.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.setThreshold" title="pyspark.mllib.classification.LogisticRegressionModel.setThreshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setThreshold</span></code></a>(value)</p></td> |
| <td><p>Sets the threshold that separates positive predictions from negative predictions.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Attributes</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.intercept" title="pyspark.mllib.classification.LogisticRegressionModel.intercept"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intercept</span></code></a></p></td> |
| <td><p>Intercept computed for this model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.numClasses" title="pyspark.mllib.classification.LogisticRegressionModel.numClasses"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numClasses</span></code></a></p></td> |
| <td><p>Number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.numFeatures" title="pyspark.mllib.classification.LogisticRegressionModel.numFeatures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numFeatures</span></code></a></p></td> |
| <td><p>Dimension of the features.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.threshold" title="pyspark.mllib.classification.LogisticRegressionModel.threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threshold</span></code></a></p></td> |
| <td><p>Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel.weights" title="pyspark.mllib.classification.LogisticRegressionModel.weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">weights</span></code></a></p></td> |
| <td><p>Weights computed for every feature.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.clearThreshold"> |
| <code class="sig-name descname">clearThreshold</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.clearThreshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Clears the threshold so that <cite>predict</cite> will output raw |
| prediction scores. It is used for binary classification only.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.load"> |
| <em class="property">classmethod </em><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sc</span><span class="p">:</span> <span class="n">pyspark.context.SparkContext</span></em>, <em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="#pyspark.mllib.classification.LogisticRegressionModel" title="pyspark.mllib.classification.LogisticRegressionModel">pyspark.mllib.classification.LogisticRegressionModel</a><a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#LogisticRegressionModel.load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load a model from the given path.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.predict"> |
| <code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>VectorLike<span class="p">, </span>pyspark.rdd.RDD<span class="p">[</span>VectorLike<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → Union<span class="p">[</span>pyspark.rdd.RDD<span class="p">[</span>Union<span class="p">[</span>int<span class="p">, </span>float<span class="p">]</span><span class="p">]</span><span class="p">, </span>int<span class="p">, </span>float<span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#LogisticRegressionModel.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.predict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Predict values for a single data point or an RDD of points |
| using the model trained.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sc</span><span class="p">:</span> <span class="n">pyspark.context.SparkContext</span></em>, <em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#LogisticRegressionModel.save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save this model to the given path.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.setThreshold"> |
| <code class="sig-name descname">setThreshold</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">value</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.setThreshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets the threshold that separates positive predictions from |
| negative predictions. An example with prediction score greater |
| than or equal to this threshold is identified as a positive, |
| and negative otherwise. It is used for binary classification |
| only.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <p class="rubric">Attributes Documentation</p> |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.intercept"> |
| <code class="sig-name descname">intercept</code><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.intercept" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Intercept computed for this model.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.numClasses"> |
| <code class="sig-name descname">numClasses</code><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.numClasses" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Number of possible outcomes for k classes classification problem |
| in Multinomial Logistic Regression.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.numFeatures"> |
| <code class="sig-name descname">numFeatures</code><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.numFeatures" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Dimension of the features.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.threshold"> |
| <code class="sig-name descname">threshold</code><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.threshold" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the threshold (if any) used for converting raw |
| prediction scores into 0/1 predictions. It is used for |
| binary classification only.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.classification.LogisticRegressionModel.weights"> |
| <code class="sig-name descname">weights</code><a class="headerlink" href="#pyspark.mllib.classification.LogisticRegressionModel.weights" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Weights computed for every feature.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
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