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| Users can call summary to print a summary of the fitted model, |
| predict to make predictions on new data, |
| and write.ml/read.ml to save/load fitted models."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> |
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| |
| <h1>Linear Regression Model</h1> |
| |
| <div class="d-none name"><code>spark.lm.Rd</code></div> |
| </div> |
| |
| <div class="ref-description section level2"> |
| <p><code>spark.lm</code> fits a linear regression model against a SparkDataFrame. |
| Users can call <code>summary</code> to print a summary of the fitted model, |
| <code>predict</code> to make predictions on new data, |
| and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models.</p> |
| </div> |
| |
| <div class="section level2"> |
| <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2> |
| <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">spark.lm</span><span class="op">(</span><span class="va">data</span>, <span class="va">formula</span>, <span class="va">...</span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'SparkDataFrame,formula'</span></span> |
| <span><span class="fu">spark.lm</span><span class="op">(</span></span> |
| <span> <span class="va">data</span>,</span> |
| <span> <span class="va">formula</span>,</span> |
| <span> maxIter <span class="op">=</span> <span class="fl">100L</span>,</span> |
| <span> regParam <span class="op">=</span> <span class="fl">0</span>,</span> |
| <span> elasticNetParam <span class="op">=</span> <span class="fl">0</span>,</span> |
| <span> tol <span class="op">=</span> <span class="fl">1e-06</span>,</span> |
| <span> standardization <span class="op">=</span> <span class="cn">TRUE</span>,</span> |
| <span> solver <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"l-bfgs"</span>, <span class="st">"normal"</span><span class="op">)</span>,</span> |
| <span> weightCol <span class="op">=</span> <span class="cn">NULL</span>,</span> |
| <span> aggregationDepth <span class="op">=</span> <span class="fl">2L</span>,</span> |
| <span> loss <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"squaredError"</span>, <span class="st">"huber"</span><span class="op">)</span>,</span> |
| <span> epsilon <span class="op">=</span> <span class="fl">1.35</span>,</span> |
| <span> stringIndexerOrderType <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"frequencyDesc"</span>, <span class="st">"frequencyAsc"</span>, <span class="st">"alphabetDesc"</span>,</span> |
| <span> <span class="st">"alphabetAsc"</span><span class="op">)</span></span> |
| <span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'LinearRegressionModel'</span></span> |
| <span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'LinearRegressionModel'</span></span> |
| <span><span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newData</span><span class="op">)</span></span> |
| <span></span> |
| <span><span class="co"># S4 method for class 'LinearRegressionModel,character'</span></span> |
| <span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">path</span>, overwrite <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> |
| </div> |
| |
| <div class="section level2"> |
| <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2> |
| |
| |
| <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt> |
| <dd><p>a <code>SparkDataFrame</code> of observations and labels for model fitting.</p></dd> |
| |
| |
| <dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt> |
| <dd><p>a symbolic description of the model to be fitted. Currently only a few formula |
| operators are supported, including '~', '.', ':', '+', and '-'.</p></dd> |
| |
| |
| <dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt> |
| <dd><p>additional arguments passed to the method.</p></dd> |
| |
| |
| <dt id="arg-maxiter">maxIter<a class="anchor" aria-label="anchor" href="#arg-maxiter"></a></dt> |
| <dd><p>maximum iteration number.</p></dd> |
| |
| |
| <dt id="arg-regparam">regParam<a class="anchor" aria-label="anchor" href="#arg-regparam"></a></dt> |
| <dd><p>the regularization parameter.</p></dd> |
| |
| |
| <dt id="arg-elasticnetparam">elasticNetParam<a class="anchor" aria-label="anchor" href="#arg-elasticnetparam"></a></dt> |
| <dd><p>the ElasticNet mixing parameter, in range [0, 1]. |
| For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.</p></dd> |
| |
| |
| <dt id="arg-tol">tol<a class="anchor" aria-label="anchor" href="#arg-tol"></a></dt> |
| <dd><p>convergence tolerance of iterations.</p></dd> |
| |
| |
| <dt id="arg-standardization">standardization<a class="anchor" aria-label="anchor" href="#arg-standardization"></a></dt> |
| <dd><p>whether to standardize the training features before fitting the model.</p></dd> |
| |
| |
| <dt id="arg-solver">solver<a class="anchor" aria-label="anchor" href="#arg-solver"></a></dt> |
| <dd><p>The solver algorithm for optimization. |
| Supported options: "l-bfgs", "normal" and "auto".</p></dd> |
| |
| |
| <dt id="arg-weightcol">weightCol<a class="anchor" aria-label="anchor" href="#arg-weightcol"></a></dt> |
| <dd><p>weight column name.</p></dd> |
| |
| |
| <dt id="arg-aggregationdepth">aggregationDepth<a class="anchor" aria-label="anchor" href="#arg-aggregationdepth"></a></dt> |
| <dd><p>suggested depth for treeAggregate (>= 2).</p></dd> |
| |
| |
| <dt id="arg-loss">loss<a class="anchor" aria-label="anchor" href="#arg-loss"></a></dt> |
| <dd><p>the loss function to be optimized. Supported options: "squaredError" and "huber".</p></dd> |
| |
| |
| <dt id="arg-epsilon">epsilon<a class="anchor" aria-label="anchor" href="#arg-epsilon"></a></dt> |
| <dd><p>the shape parameter to control the amount of robustness.</p></dd> |
| |
| |
| <dt id="arg-stringindexerordertype">stringIndexerOrderType<a class="anchor" aria-label="anchor" href="#arg-stringindexerordertype"></a></dt> |
| <dd><p>how to order categories of a string feature column. This is used to |
| decide the base level of a string feature as the last category |
| after ordering is dropped when encoding strings. Supported options |
| are "frequencyDesc", "frequencyAsc", "alphabetDesc", and |
| "alphabetAsc". The default value is "frequencyDesc". When the |
| ordering is set to "alphabetDesc", this drops the same category |
| as R when encoding strings.</p></dd> |
| |
| |
| <dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt> |
| <dd><p>a Linear Regression Model model fitted by <code>spark.lm</code>.</p></dd> |
| |
| |
| <dt id="arg-newdata">newData<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt> |
| <dd><p>a SparkDataFrame for testing.</p></dd> |
| |
| |
| <dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt> |
| <dd><p>The directory where the model is saved.</p></dd> |
| |
| |
| <dt id="arg-overwrite">overwrite<a class="anchor" aria-label="anchor" href="#arg-overwrite"></a></dt> |
| <dd><p>Overwrites or not if the output path already exists. Default is FALSE |
| which means throw exception if the output path exists.</p></dd> |
| |
| </dl></div> |
| <div class="section level2"> |
| <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2> |
| <p><code>spark.lm</code> returns a fitted Linear Regression Model.</p> |
| <p><code>summary</code> returns summary information of the fitted model, which is a list.</p> |
| <p><code>predict</code> returns the predicted values based on a LinearRegressionModel.</p> |
| </div> |
| <div class="section level2"> |
| <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2> |
| <p>spark.lm since 3.1.0</p> |
| <p>summary(LinearRegressionModel) since 3.1.0</p> |
| <p>predict(LinearRegressionModel) since 3.1.0</p> |
| <p>write.ml(LinearRegressionModel, character) since 3.1.0</p> |
| </div> |
| <div class="section level2"> |
| <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2> |
| <div class="dont-index"><p><a href="read.ml.html">read.ml</a></p></div> |
| </div> |
| |
| <div class="section level2"> |
| <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2> |
| <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span> |
| <span class="r-in"><span><span class="va">df</span> <span class="op"><-</span> <span class="fu"><a href="read.df.html">read.df</a></span><span class="op">(</span><span class="st">"data/mllib/sample_linear_regression_data.txt"</span>, source <span class="op">=</span> <span class="st">"libsvm"</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># fit Linear Regression Model</span></span></span> |
| <span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu">spark.lm</span><span class="op">(</span><span class="va">df</span>, <span class="va">label</span> <span class="op">~</span> <span class="va">features</span>, regParam <span class="op">=</span> <span class="fl">0.01</span>, maxIter <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># get the summary of the model</span></span></span> |
| <span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># make predictions</span></span></span> |
| <span class="r-in"><span><span class="va">predictions</span> <span class="op"><-</span> <span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">df</span><span class="op">)</span></span></span> |
| <span class="r-in"><span></span></span> |
| <span class="r-in"><span><span class="co"># save and load the model</span></span></span> |
| <span class="r-in"><span><span class="va">path</span> <span class="op"><-</span> <span class="st">"path/to/model"</span></span></span> |
| <span class="r-in"><span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">path</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="va">savedModel</span> <span class="op"><-</span> <span class="fu"><a href="read.ml.html">read.ml</a></span><span class="op">(</span><span class="va">path</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">savedModel</span><span class="op">)</span></span></span> |
| <span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span> |
| </code></pre></div> |
| </div> |
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