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       <h1>Factorization Machines Classification Model</h1>

       <div class="d-none name"><code>spark.fmClassifier.Rd</code></div>
     </div>

     <div class="ref-description section level2">
     <p><code>spark.fmClassifier</code> fits a factorization classification 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.
 Only categorical data is supported.</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.fmClassifier</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.fmClassifier</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
 <span>  <span class="va">formula</span>,</span>
 <span>  factorSize <span class="op">=</span> <span class="fl">8</span>,</span>
 <span>  fitLinear <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>  regParam <span class="op">=</span> <span class="fl">0</span>,</span>
 <span>  miniBatchFraction <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  initStd <span class="op">=</span> <span class="fl">0.01</span>,</span>
 <span>  maxIter <span class="op">=</span> <span class="fl">100</span>,</span>
 <span>  stepSize <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  tol <span class="op">=</span> <span class="fl">1e-06</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">"adamW"</span>, <span class="st">"gd"</span><span class="op">)</span>,</span>
 <span>  thresholds <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  seed <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  handleInvalid <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">"error"</span>, <span class="st">"keep"</span>, <span class="st">"skip"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S4 method for class 'FMClassificationModel'</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 'FMClassificationModel'</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 'FMClassificationModel,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-factorsize">factorSize<a class="anchor" aria-label="anchor" href="#arg-factorsize"></a></dt>
 <dd><p>dimensionality of the factors.</p></dd>


 <dt id="arg-fitlinear">fitLinear<a class="anchor" aria-label="anchor" href="#arg-fitlinear"></a></dt>
 <dd><p>whether to fit linear term.  # TODO Can we express this with formula?</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-minibatchfraction">miniBatchFraction<a class="anchor" aria-label="anchor" href="#arg-minibatchfraction"></a></dt>
 <dd><p>the mini-batch fraction parameter.</p></dd>


 <dt id="arg-initstd">initStd<a class="anchor" aria-label="anchor" href="#arg-initstd"></a></dt>
 <dd><p>the standard deviation of initial coefficients.</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-stepsize">stepSize<a class="anchor" aria-label="anchor" href="#arg-stepsize"></a></dt>
 <dd><p>stepSize parameter.</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-solver">solver<a class="anchor" aria-label="anchor" href="#arg-solver"></a></dt>
 <dd><p>solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".</p></dd>


 <dt id="arg-thresholds">thresholds<a class="anchor" aria-label="anchor" href="#arg-thresholds"></a></dt>
 <dd><p>in binary classification, in range [0, 1]. If the estimated probability of
 class label 1 is &gt; threshold, then predict 1, else 0. A high threshold
 encourages the model to predict 0 more often; a low threshold encourages the
 model to predict 1 more often. Note: Setting this with threshold p is
 equivalent to setting thresholds c(1-p, p).</p></dd>


 <dt id="arg-seed">seed<a class="anchor" aria-label="anchor" href="#arg-seed"></a></dt>
 <dd><p>seed parameter for weights initialization.</p></dd>


 <dt id="arg-handleinvalid">handleInvalid<a class="anchor" aria-label="anchor" href="#arg-handleinvalid"></a></dt>
 <dd><p>How to handle invalid data (unseen labels or NULL values) in features and
 label column of string type.
 Supported options: "skip" (filter out rows with invalid data),
                    "error" (throw an error), "keep" (put invalid data in
                    a special additional bucket, at index numLabels). Default
                    is "error".</p></dd>


 <dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
 <dd><p>a FM Classification model fitted by <code>spark.fmClassifier</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.fmClassifier</code> returns a fitted Factorization Machines Classification 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 FM Classification model.</p>
     </div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>spark.fmClassifier since 3.1.0</p>
 <p>summary(FMClassificationModel) since 3.1.0</p>
 <p>predict(FMClassificationModel) since 3.1.0</p>
 <p>write.ml(FMClassificationModel, 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">&lt;-</span> <span class="fu"><a href="read.df.html">read.df</a></span><span class="op">(</span><span class="st">"data/mllib/sample_binary_classification_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 Factorization Machines Classification Model</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">spark.fmClassifier</span><span class="op">(</span></span></span>
 <span class="r-in"><span>           <span class="va">df</span>, <span class="va">label</span> <span class="op">~</span> <span class="va">features</span>,</span></span>
 <span class="r-in"><span>           regParam <span class="op">=</span> <span class="fl">0.01</span>, maxIter <span class="op">=</span> <span class="fl">10</span>, fitLinear <span class="op">=</span> <span class="cn">TRUE</span></span></span>
 <span class="r-in"><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">&lt;-</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">&lt;-</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">&lt;-</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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	Only categorical data is supported."><!-- 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>Factorization Machines Classification Model</h1>

	<div class="d-none name"><code>spark.fmClassifier.Rd</code></div>
	</div>

	<div class="ref-description section level2">
	<p><code>spark.fmClassifier</code> fits a factorization classification 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.
	Only categorical data is supported.</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.fmClassifier</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.fmClassifier</span><span class="op">(</span></span>
	<span> <span class="va">data</span>,</span>
	<span> <span class="va">formula</span>,</span>
	<span> factorSize <span class="op">=</span> <span class="fl">8</span>,</span>
	<span> fitLinear <span class="op">=</span> <span class="cn">TRUE</span>,</span>
	<span> regParam <span class="op">=</span> <span class="fl">0</span>,</span>
	<span> miniBatchFraction <span class="op">=</span> <span class="fl">1</span>,</span>
	<span> initStd <span class="op">=</span> <span class="fl">0.01</span>,</span>
	<span> maxIter <span class="op">=</span> <span class="fl">100</span>,</span>
	<span> stepSize <span class="op">=</span> <span class="fl">1</span>,</span>
	<span> tol <span class="op">=</span> <span class="fl">1e-06</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">"adamW"</span>, <span class="st">"gd"</span><span class="op">)</span>,</span>
	<span> thresholds <span class="op">=</span> <span class="cn">NULL</span>,</span>
	<span> seed <span class="op">=</span> <span class="cn">NULL</span>,</span>
	<span> handleInvalid <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">"error"</span>, <span class="st">"keep"</span>, <span class="st">"skip"</span><span class="op">)</span></span>
	<span><span class="op">)</span></span>
	<span></span>
	<span><span class="co"># S4 method for class 'FMClassificationModel'</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 'FMClassificationModel'</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 'FMClassificationModel,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-factorsize">factorSize<a class="anchor" aria-label="anchor" href="#arg-factorsize"></a></dt>
	<dd><p>dimensionality of the factors.</p></dd>


	<dt id="arg-fitlinear">fitLinear<a class="anchor" aria-label="anchor" href="#arg-fitlinear"></a></dt>
	<dd><p>whether to fit linear term. # TODO Can we express this with formula?</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-minibatchfraction">miniBatchFraction<a class="anchor" aria-label="anchor" href="#arg-minibatchfraction"></a></dt>
	<dd><p>the mini-batch fraction parameter.</p></dd>


	<dt id="arg-initstd">initStd<a class="anchor" aria-label="anchor" href="#arg-initstd"></a></dt>
	<dd><p>the standard deviation of initial coefficients.</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-stepsize">stepSize<a class="anchor" aria-label="anchor" href="#arg-stepsize"></a></dt>
	<dd><p>stepSize parameter.</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-solver">solver<a class="anchor" aria-label="anchor" href="#arg-solver"></a></dt>
	<dd><p>solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".</p></dd>


	<dt id="arg-thresholds">thresholds<a class="anchor" aria-label="anchor" href="#arg-thresholds"></a></dt>
	<dd><p>in binary classification, in range [0, 1]. If the estimated probability of
	class label 1 is > threshold, then predict 1, else 0. A high threshold
	encourages the model to predict 0 more often; a low threshold encourages the
	model to predict 1 more often. Note: Setting this with threshold p is
	equivalent to setting thresholds c(1-p, p).</p></dd>


	<dt id="arg-seed">seed<a class="anchor" aria-label="anchor" href="#arg-seed"></a></dt>
	<dd><p>seed parameter for weights initialization.</p></dd>


	<dt id="arg-handleinvalid">handleInvalid<a class="anchor" aria-label="anchor" href="#arg-handleinvalid"></a></dt>
	<dd><p>How to handle invalid data (unseen labels or NULL values) in features and
	label column of string type.
	Supported options: "skip" (filter out rows with invalid data),
	"error" (throw an error), "keep" (put invalid data in
	a special additional bucket, at index numLabels). Default
	is "error".</p></dd>


	<dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
	<dd><p>a FM Classification model fitted by <code>spark.fmClassifier</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.fmClassifier</code> returns a fitted Factorization Machines Classification 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 FM Classification model.</p>
	</div>
	<div class="section level2">
	<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
	<p>spark.fmClassifier since 3.1.0</p>
	<p>summary(FMClassificationModel) since 3.1.0</p>
	<p>predict(FMClassificationModel) since 3.1.0</p>
	<p>write.ml(FMClassificationModel, 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_binary_classification_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 Factorization Machines Classification Model</span></span></span>
	<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu">spark.fmClassifier</span><span class="op">(</span></span></span>
	<span class="r-in"><span> <span class="va">df</span>, <span class="va">label</span> <span class="op">~</span> <span class="va">features</span>,</span></span>
	<span class="r-in"><span> regParam <span class="op">=</span> <span class="fl">0.01</span>, maxIter <span class="op">=</span> <span class="fl">10</span>, fitLinear <span class="op">=</span> <span class="cn">TRUE</span></span></span>
	<span class="r-in"><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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