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<div class="subTitle">org.apache.spark.ml.regression</div>
<h2 title="Class LinearRegression" class="title">Class LinearRegression</h2>
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<li>Object</li>
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<li><a href="../../../../../org/apache/spark/ml/PipelineStage.html" title="class in org.apache.spark.ml">org.apache.spark.ml.PipelineStage</a></li>
<li>
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<li><a href="../../../../../org/apache/spark/ml/Estimator.html" title="class in org.apache.spark.ml">org.apache.spark.ml.Estimator</a>&lt;M&gt;</li>
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<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/ml/Predictor.html" title="class in org.apache.spark.ml">org.apache.spark.ml.Predictor</a>&lt;FeaturesType,Learner,M&gt;</li>
<li>
<ul class="inheritance">
<li><a href="../../../../../org/apache/spark/ml/regression/Regressor.html" title="class in org.apache.spark.ml.regression">org.apache.spark.ml.regression.Regressor</a>&lt;<a href="../../../../../org/apache/spark/ml/linalg/Vector.html" title="interface in org.apache.spark.ml.linalg">Vector</a>,<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>,<a href="../../../../../org/apache/spark/ml/regression/LinearRegressionModel.html" title="class in org.apache.spark.ml.regression">LinearRegressionModel</a>&gt;</li>
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<li>org.apache.spark.ml.regression.LinearRegression</li>
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<dt>All Implemented Interfaces:</dt>
<dd>java.io.Serializable, org.apache.spark.internal.Logging, <a href="../../../../../org/apache/spark/ml/param/Params.html" title="interface in org.apache.spark.ml.param">Params</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasAggregationDepth.html" title="interface in org.apache.spark.ml.param.shared">HasAggregationDepth</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasElasticNetParam.html" title="interface in org.apache.spark.ml.param.shared">HasElasticNetParam</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasFeaturesCol.html" title="interface in org.apache.spark.ml.param.shared">HasFeaturesCol</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasFitIntercept.html" title="interface in org.apache.spark.ml.param.shared">HasFitIntercept</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasLabelCol.html" title="interface in org.apache.spark.ml.param.shared">HasLabelCol</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasLoss.html" title="interface in org.apache.spark.ml.param.shared">HasLoss</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasMaxBlockSizeInMB.html" title="interface in org.apache.spark.ml.param.shared">HasMaxBlockSizeInMB</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasMaxIter.html" title="interface in org.apache.spark.ml.param.shared">HasMaxIter</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasPredictionCol.html" title="interface in org.apache.spark.ml.param.shared">HasPredictionCol</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasRegParam.html" title="interface in org.apache.spark.ml.param.shared">HasRegParam</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasSolver.html" title="interface in org.apache.spark.ml.param.shared">HasSolver</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasStandardization.html" title="interface in org.apache.spark.ml.param.shared">HasStandardization</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasTol.html" title="interface in org.apache.spark.ml.param.shared">HasTol</a>, <a href="../../../../../org/apache/spark/ml/param/shared/HasWeightCol.html" title="interface in org.apache.spark.ml.param.shared">HasWeightCol</a>, <a href="../../../../../org/apache/spark/ml/PredictorParams.html" title="interface in org.apache.spark.ml">PredictorParams</a>, <a href="../../../../../org/apache/spark/ml/regression/LinearRegressionParams.html" title="interface in org.apache.spark.ml.regression">LinearRegressionParams</a>, <a href="../../../../../org/apache/spark/ml/util/DefaultParamsWritable.html" title="interface in org.apache.spark.ml.util">DefaultParamsWritable</a>, <a href="../../../../../org/apache/spark/ml/util/Identifiable.html" title="interface in org.apache.spark.ml.util">Identifiable</a>, <a href="../../../../../org/apache/spark/ml/util/MLWritable.html" title="interface in org.apache.spark.ml.util">MLWritable</a></dd>
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<pre>public class <span class="typeNameLabel">LinearRegression</span>
extends <a href="../../../../../org/apache/spark/ml/regression/Regressor.html" title="class in org.apache.spark.ml.regression">Regressor</a>&lt;<a href="../../../../../org/apache/spark/ml/linalg/Vector.html" title="interface in org.apache.spark.ml.linalg">Vector</a>,<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>,<a href="../../../../../org/apache/spark/ml/regression/LinearRegressionModel.html" title="class in org.apache.spark.ml.regression">LinearRegressionModel</a>&gt;
implements <a href="../../../../../org/apache/spark/ml/regression/LinearRegressionParams.html" title="interface in org.apache.spark.ml.regression">LinearRegressionParams</a>, <a href="../../../../../org/apache/spark/ml/util/DefaultParamsWritable.html" title="interface in org.apache.spark.ml.util">DefaultParamsWritable</a>, org.apache.spark.internal.Logging</pre>
<div class="block">Linear regression.
<p>
The learning objective is to minimize the specified loss function, with regularization.
This supports two kinds of loss:
- squaredError (a.k.a squared loss)
- huber (a hybrid of squared error for relatively small errors and absolute error for
relatively large ones, and we estimate the scale parameter from training data)
<p>
This supports multiple types of regularization:
- none (a.k.a. ordinary least squares)
- L2 (ridge regression)
- L1 (Lasso)
- L2 + L1 (elastic net)
<p>
The squared error objective function is:
<p>
<blockquote>
$$
\begin{align}
\min_{w}\frac{1}{2n}{\sum_{i=1}^n(X_{i}w - y_{i})^{2} +
\lambda\left[\frac{1-\alpha}{2}{||w||_{2}}^{2} + \alpha{||w||_{1}}\right]}
\end{align}
$$
</blockquote>
<p>
The huber objective function is:
<p>
<blockquote>
$$
\begin{align}
\min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma +
H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2}
\end{align}
$$
</blockquote>
<p>
where
<p>
<blockquote>
$$
\begin{align}
H_m(z) = \begin{cases}
z^2, &amp; \text {if } |z| &amp;lt; \epsilon, \\
2\epsilon|z| - \epsilon^2, &amp; \text{otherwise}
\end{cases}
\end{align}
$$
</blockquote>
<p>
Since 3.1.0, it supports stacking instances into blocks and using GEMV for
better performance.
The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.
<p>
Note: Fitting with huber loss only supports none and L2 regularization.</div>
<dl>
<dt><span class="seeLabel">See Also:</span></dt>
<dd><a href="../../../../../serialized-form.html#org.apache.spark.ml.regression.LinearRegression">Serialized Form</a></dd>
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<td class="colOne"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#LinearRegression--">LinearRegression</a></span>()</code>&nbsp;</td>
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<td class="colOne"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#LinearRegression-java.lang.String-">LinearRegression</a></span>(String&nbsp;uid)</code>&nbsp;</td>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/IntParam.html" title="class in org.apache.spark.ml.param">IntParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#aggregationDepth--">aggregationDepth</a></span>()</code>
<div class="block">Param for suggested depth for treeAggregate (&amp;gt;= 2).</div>
</td>
</tr>
<tr id="i1" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#copy-org.apache.spark.ml.param.ParamMap-">copy</a></span>(<a href="../../../../../org/apache/spark/ml/param/ParamMap.html" title="class in org.apache.spark.ml.param">ParamMap</a>&nbsp;extra)</code>
<div class="block">Creates a copy of this instance with the same UID and some extra params.</div>
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<tr id="i2" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#elasticNetParam--">elasticNetParam</a></span>()</code>
<div class="block">Param for the ElasticNet mixing parameter, in range [0, 1].</div>
</td>
</tr>
<tr id="i3" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#epsilon--">epsilon</a></span>()</code>
<div class="block">The shape parameter to control the amount of robustness.</div>
</td>
</tr>
<tr id="i4" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/BooleanParam.html" title="class in org.apache.spark.ml.param">BooleanParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#fitIntercept--">fitIntercept</a></span>()</code>
<div class="block">Param for whether to fit an intercept term.</div>
</td>
</tr>
<tr id="i5" class="rowColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#load-java.lang.String-">load</a></span>(String&nbsp;path)</code>&nbsp;</td>
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<tr id="i6" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/Param.html" title="class in org.apache.spark.ml.param">Param</a>&lt;String&gt;</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#loss--">loss</a></span>()</code>
<div class="block">The loss function to be optimized.</div>
</td>
</tr>
<tr id="i7" class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#MAX_FEATURES_FOR_NORMAL_SOLVER--">MAX_FEATURES_FOR_NORMAL_SOLVER</a></span>()</code>
<div class="block">When using <code>LinearRegression.solver</code> == "normal", the solver must limit the number of
features to at most this number.</div>
</td>
</tr>
<tr id="i8" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#maxBlockSizeInMB--">maxBlockSizeInMB</a></span>()</code>
<div class="block">Param for Maximum memory in MB for stacking input data into blocks.</div>
</td>
</tr>
<tr id="i9" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/IntParam.html" title="class in org.apache.spark.ml.param">IntParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#maxIter--">maxIter</a></span>()</code>
<div class="block">Param for maximum number of iterations (&amp;gt;= 0).</div>
</td>
</tr>
<tr id="i10" class="altColor">
<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/ml/util/MLReader.html" title="class in org.apache.spark.ml.util">MLReader</a>&lt;T&gt;</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#read--">read</a></span>()</code>&nbsp;</td>
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<tr id="i11" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#regParam--">regParam</a></span>()</code>
<div class="block">Param for regularization parameter (&amp;gt;= 0).</div>
</td>
</tr>
<tr id="i12" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setAggregationDepth-int-">setAggregationDepth</a></span>(int&nbsp;value)</code>
<div class="block">Suggested depth for treeAggregate (greater than or equal to 2).</div>
</td>
</tr>
<tr id="i13" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setElasticNetParam-double-">setElasticNetParam</a></span>(double&nbsp;value)</code>
<div class="block">Set the ElasticNet mixing parameter.</div>
</td>
</tr>
<tr id="i14" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setEpsilon-double-">setEpsilon</a></span>(double&nbsp;value)</code>
<div class="block">Sets the value of param <code>epsilon</code>.</div>
</td>
</tr>
<tr id="i15" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setFitIntercept-boolean-">setFitIntercept</a></span>(boolean&nbsp;value)</code>
<div class="block">Set if we should fit the intercept.</div>
</td>
</tr>
<tr id="i16" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setLoss-java.lang.String-">setLoss</a></span>(String&nbsp;value)</code>
<div class="block">Sets the value of param <code>loss</code>.</div>
</td>
</tr>
<tr id="i17" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setMaxBlockSizeInMB-double-">setMaxBlockSizeInMB</a></span>(double&nbsp;value)</code>
<div class="block">Sets the value of param <code>maxBlockSizeInMB</code>.</div>
</td>
</tr>
<tr id="i18" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setMaxIter-int-">setMaxIter</a></span>(int&nbsp;value)</code>
<div class="block">Set the maximum number of iterations.</div>
</td>
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<tr id="i19" class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setRegParam-double-">setRegParam</a></span>(double&nbsp;value)</code>
<div class="block">Set the regularization parameter.</div>
</td>
</tr>
<tr id="i20" class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html#setSolver-java.lang.String-">setSolver</a></span>(String&nbsp;value)</code>
<div class="block">Set the solver algorithm used for optimization.</div>
</td>
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<pre>public static&nbsp;int&nbsp;MAX_FEATURES_FOR_NORMAL_SOLVER()</pre>
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<div class="block">The loss function to be optimized.
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<pre>public final&nbsp;<a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a>&nbsp;epsilon()</pre>
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<div class="block">The shape parameter to control the amount of robustness. Must be &amp;gt; 1.0.
At larger values of epsilon, the huber criterion becomes more similar to least squares
regression; for small values of epsilon, the criterion is more similar to L1 regression.
Default is 1.35 to get as much robustness as possible while retaining
95% statistical efficiency for normally distributed data. It matches sklearn
HuberRegressor and is "M" from <a href="http://statweb.stanford.edu/~owen/reports/hhu.pdf">
A robust hybrid of lasso and ridge regression</a>.
Only valid when "loss" is "huber".
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<div class="block">Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be &amp;gt;= 0..</div>
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<div class="block">Param for suggested depth for treeAggregate (&amp;gt;= 2).</div>
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<div class="block">Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.</div>
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<div class="block">Param for whether to standardize the training features before fitting the model.</div>
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<div class="block">Param for whether to fit an intercept term.</div>
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<div class="block">Param for the convergence tolerance for iterative algorithms (&amp;gt;= 0).</div>
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<pre>public final&nbsp;<a href="../../../../../org/apache/spark/ml/param/IntParam.html" title="class in org.apache.spark.ml.param">IntParam</a>&nbsp;maxIter()</pre>
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<div class="block">Param for maximum number of iterations (&amp;gt;= 0).</div>
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<pre>public final&nbsp;<a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a>&nbsp;elasticNetParam()</pre>
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<div class="block">Param for 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.</div>
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<pre>public final&nbsp;<a href="../../../../../org/apache/spark/ml/param/DoubleParam.html" title="class in org.apache.spark.ml.param">DoubleParam</a>&nbsp;regParam()</pre>
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<div class="block">Param for regularization parameter (&amp;gt;= 0).</div>
<dl>
<dt><span class="overrideSpecifyLabel">Specified by:</span></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/param/shared/HasRegParam.html#regParam--">regParam</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../org/apache/spark/ml/param/shared/HasRegParam.html" title="interface in org.apache.spark.ml.param.shared">HasRegParam</a></code></dd>
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<pre>public&nbsp;String&nbsp;uid()</pre>
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<div class="block">An immutable unique ID for the object and its derivatives.</div>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setRegParam(double&nbsp;value)</pre>
<div class="block">Set the regularization parameter.
Default is 0.0.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setFitIntercept(boolean&nbsp;value)</pre>
<div class="block">Set if we should fit the intercept.
Default is true.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setStandardization(boolean&nbsp;value)</pre>
<div class="block">Whether to standardize the training features before fitting the model.
The coefficients of models will be always returned on the original scale,
so it will be transparent for users.
Default is true.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>(undocumented)</dd>
<dt><span class="simpleTagLabel">Note:</span></dt>
<dd>With/without standardization, the models should be always converged
to the same solution when no regularization is applied. In R's GLMNET package,
the default behavior is true as well.
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setElasticNetParam(double&nbsp;value)</pre>
<div class="block">Set the ElasticNet mixing parameter.
For alpha = 0, the penalty is an L2 penalty.
For alpha = 1, it is an L1 penalty.
For alpha in (0,1), the penalty is a combination of L1 and L2.
Default is 0.0 which is an L2 penalty.
<p>
Note: Fitting with huber loss only supports None and L2 regularization,
so throws exception if this param is non-zero value.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setMaxIter(int&nbsp;value)</pre>
<div class="block">Set the maximum number of iterations.
Default is 100.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setTol(double&nbsp;value)</pre>
<div class="block">Set the convergence tolerance of iterations.
Smaller value will lead to higher accuracy with the cost of more iterations.
Default is 1E-6.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setWeightCol(String&nbsp;value)</pre>
<div class="block">Whether to over-/under-sample training instances according to the given weights in weightCol.
If not set or empty, all instances are treated equally (weight 1.0).
Default is not set, so all instances have weight one.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setSolver(String&nbsp;value)</pre>
<div class="block">Set the solver algorithm used for optimization.
In case of linear regression, this can be "l-bfgs", "normal" and "auto".
- "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton
optimization method.
- "normal" denotes using Normal Equation as an analytical solution to the linear regression
problem. This solver is limited to <code>LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER</code>.
- "auto" (default) means that the solver algorithm is selected automatically.
The Normal Equations solver will be used when possible, but this will automatically fall
back to iterative optimization methods when needed.
<p>
Note: Fitting with huber loss doesn't support normal solver,
so throws exception if this param was set with "normal".</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setAggregationDepth(int&nbsp;value)</pre>
<div class="block">Suggested depth for treeAggregate (greater than or equal to 2).
If the dimensions of features or the number of partitions are large,
this param could be adjusted to a larger size.
Default is 2.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>(undocumented)</dd>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setLoss(String&nbsp;value)</pre>
<div class="block">Sets the value of param <code>loss</code>.
Default is "squaredError".
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>(undocumented)</dd>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setEpsilon(double&nbsp;value)</pre>
<div class="block">Sets the value of param <code>epsilon</code>.
Default is 1.35.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>(undocumented)</dd>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;setMaxBlockSizeInMB(double&nbsp;value)</pre>
<div class="block">Sets the value of param <code>maxBlockSizeInMB</code>.
Default is 0.0, then 1.0 MB will be chosen.
<p></div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>value</code> - (undocumented)</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public&nbsp;<a href="../../../../../org/apache/spark/ml/regression/LinearRegression.html" title="class in org.apache.spark.ml.regression">LinearRegression</a>&nbsp;copy(<a href="../../../../../org/apache/spark/ml/param/ParamMap.html" title="class in org.apache.spark.ml.param">ParamMap</a>&nbsp;extra)</pre>
<div class="block"><span class="descfrmTypeLabel">Description copied from interface:&nbsp;<code><a href="../../../../../org/apache/spark/ml/param/Params.html#copy-org.apache.spark.ml.param.ParamMap-">Params</a></code></span></div>
<div class="block">Creates a copy of this instance with the same UID and some extra params.
Subclasses should implement this method and set the return type properly.
See <code>defaultCopy()</code>.</div>
<dl>
<dt><span class="overrideSpecifyLabel">Specified by:</span></dt>
<dd><code><a href="../../../../../org/apache/spark/ml/param/Params.html#copy-org.apache.spark.ml.param.ParamMap-">copy</a></code>&nbsp;in interface&nbsp;<code><a href="../../../../../org/apache/spark/ml/param/Params.html" title="interface in org.apache.spark.ml.param">Params</a></code></dd>
<dt><span class="overrideSpecifyLabel">Specified by:</span></dt>
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