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Lrnr_svm.html
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Lrnr_svm.html
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<h1>Support Vector Machines</h1>
<small class="dont-index">Source: <a href='https://github.com/tlverse/sl3/blob/master/R/Lrnr_svm.R'><code>R/Lrnr_svm.R</code></a></small>
<div class="hidden name"><code>Lrnr_svm.Rd</code></div>
</div>
<div class="ref-description">
<p>This learner provides fitting procedures for support vector machines, using
the routines from <span class="pkg">e1071</span> (described in Meyer et al. (2021)
and Chang and Lin (2011)
, the core library to which <span class="pkg">e1071</span>
is an interface) through a call to the function <code><a href='https://rdrr.io/pkg/e1071/man/svm.html'>svm</a></code>.</p>
</div>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>An <code><a href='https://r6.r-lib.org/reference/R6Class.html'>R6Class</a></code> object inheriting from
<code><a href='Lrnr_base.html'>Lrnr_base</a></code>.</p>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>A learner object inheriting from <code><a href='Lrnr_base.html'>Lrnr_base</a></code> with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of <code><a href='Lrnr_base.html'>Lrnr_base</a></code>.</p>
<h2 class="hasAnchor" id="parameters"><a class="anchor" href="#parameters"></a>Parameters</h2>
<ul>
<li><p><code>scale = TRUE</code>: A logical vector indicating the variables to be
scaled. For a detailed description, please consult the documentation
for <code><a href='https://rdrr.io/pkg/e1071/man/svm.html'>svm</a></code>.</p></li>
<li><p><code>type = NULL</code>: SVMs can be used as a classification machine, as a
a regression machine, or for novelty detection. Depending of whether
the outcome is a factor or not, the default setting for this argument
is "C-classification" or "eps-regression", respectively. This may be
overwritten by setting an explicit value. For a full set of options,
please consult the documentation for <code><a href='https://rdrr.io/pkg/e1071/man/svm.html'>svm</a></code>.</p></li>
<li><p><code>kernel = "radial"</code>: The kernel used in training and predicting.
You may consider changing some of the optional parameters, depending
on the kernel type. Kernel options include: "linear", "polynomial",
"radial" (the default), "sigmoid". For a detailed description, consult
the documentation for <code><a href='https://rdrr.io/pkg/e1071/man/svm.html'>svm</a></code>.</p></li>
<li><p><code>fitted = TRUE</code>: Logical indicating whether the fitted values
should be computed and included in the model fit object or not.</p></li>
<li><p><code>probability = FALSE</code>: Logical indicating whether the model should
allow for probability predictions.</p></li>
<li><p><code>...</code>: Other parameters passed to <code><a href='https://rdrr.io/pkg/e1071/man/svm.html'>svm</a></code>. See its
documentation for details.</p></li>
</ul>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>Chang C, Lin C (2011).
“LIBSVM: A library for support vector machines.”
<em>ACM Transactions on Intelligent Systems and Technology</em>, <b>2</b>(3), 27:1--27:27.
Software available at <a href='http://www.csie.ntu.edu.tw/~cjlin/libsvm'>http://www.csie.ntu.edu.tw/~cjlin/libsvm</a>.<br /><br /> Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2021).
<em>e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien</em>.
R package version 1.7-6, <a href='https://CRAN.R-project.org/package=e1071'>https://CRAN.R-project.org/package=e1071</a>.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p>Other Learners:
<code><a href='Custom_chain.html'>Custom_chain</a></code>,
<code><a href='Lrnr_HarmonicReg.html'>Lrnr_HarmonicReg</a></code>,
<code><a href='Lrnr_arima.html'>Lrnr_arima</a></code>,
<code><a href='Lrnr_bartMachine.html'>Lrnr_bartMachine</a></code>,
<code><a href='Lrnr_base.html'>Lrnr_base</a></code>,
<code><a href='Lrnr_bayesglm.html'>Lrnr_bayesglm</a></code>,
<code><a href='Lrnr_bilstm.html'>Lrnr_bilstm</a></code>,
<code><a href='Lrnr_caret.html'>Lrnr_caret</a></code>,
<code><a href='Lrnr_cv_selector.html'>Lrnr_cv_selector</a></code>,
<code><a href='Lrnr_cv.html'>Lrnr_cv</a></code>,
<code><a href='Lrnr_dbarts.html'>Lrnr_dbarts</a></code>,
<code><a href='Lrnr_define_interactions.html'>Lrnr_define_interactions</a></code>,
<code><a href='Lrnr_density_discretize.html'>Lrnr_density_discretize</a></code>,
<code><a href='Lrnr_density_hse.html'>Lrnr_density_hse</a></code>,
<code><a href='Lrnr_density_semiparametric.html'>Lrnr_density_semiparametric</a></code>,
<code><a href='Lrnr_earth.html'>Lrnr_earth</a></code>,
<code><a href='Lrnr_expSmooth.html'>Lrnr_expSmooth</a></code>,
<code><a href='Lrnr_gam.html'>Lrnr_gam</a></code>,
<code><a href='Lrnr_gbm.html'>Lrnr_gbm</a></code>,
<code><a href='Lrnr_glm_fast.html'>Lrnr_glm_fast</a></code>,
<code><a href='Lrnr_glmnet.html'>Lrnr_glmnet</a></code>,
<code><a href='Lrnr_glm.html'>Lrnr_glm</a></code>,
<code><a href='Lrnr_grf.html'>Lrnr_grf</a></code>,
<code><a href='Lrnr_gru_keras.html'>Lrnr_gru_keras</a></code>,
<code><a href='Lrnr_gts.html'>Lrnr_gts</a></code>,
<code><a href='Lrnr_h2o_grid.html'>Lrnr_h2o_grid</a></code>,
<code><a href='Lrnr_hal9001.html'>Lrnr_hal9001</a></code>,
<code><a href='Lrnr_haldensify.html'>Lrnr_haldensify</a></code>,
<code><a href='Lrnr_hts.html'>Lrnr_hts</a></code>,
<code><a href='Lrnr_independent_binomial.html'>Lrnr_independent_binomial</a></code>,
<code><a href='Lrnr_lightgbm.html'>Lrnr_lightgbm</a></code>,
<code><a href='Lrnr_lstm_keras.html'>Lrnr_lstm_keras</a></code>,
<code><a href='Lrnr_mean.html'>Lrnr_mean</a></code>,
<code><a href='Lrnr_multiple_ts.html'>Lrnr_multiple_ts</a></code>,
<code><a href='Lrnr_multivariate.html'>Lrnr_multivariate</a></code>,
<code><a href='Lrnr_nnet.html'>Lrnr_nnet</a></code>,
<code><a href='Lrnr_nnls.html'>Lrnr_nnls</a></code>,
<code><a href='Lrnr_optim.html'>Lrnr_optim</a></code>,
<code><a href='Lrnr_pca.html'>Lrnr_pca</a></code>,
<code><a href='Lrnr_pkg_SuperLearner.html'>Lrnr_pkg_SuperLearner</a></code>,
<code><a href='Lrnr_polspline.html'>Lrnr_polspline</a></code>,
<code><a href='Lrnr_pooled_hazards.html'>Lrnr_pooled_hazards</a></code>,
<code><a href='Lrnr_randomForest.html'>Lrnr_randomForest</a></code>,
<code><a href='Lrnr_ranger.html'>Lrnr_ranger</a></code>,
<code><a href='Lrnr_revere_task.html'>Lrnr_revere_task</a></code>,
<code><a href='Lrnr_rpart.html'>Lrnr_rpart</a></code>,
<code><a href='Lrnr_rugarch.html'>Lrnr_rugarch</a></code>,
<code><a href='Lrnr_screener_augment.html'>Lrnr_screener_augment</a></code>,
<code><a href='Lrnr_screener_coefs.html'>Lrnr_screener_coefs</a></code>,
<code><a href='Lrnr_screener_correlation.html'>Lrnr_screener_correlation</a></code>,
<code><a href='Lrnr_screener_importance.html'>Lrnr_screener_importance</a></code>,
<code><a href='Lrnr_sl.html'>Lrnr_sl</a></code>,
<code><a href='Lrnr_solnp_density.html'>Lrnr_solnp_density</a></code>,
<code><a href='Lrnr_solnp.html'>Lrnr_solnp</a></code>,
<code><a href='Lrnr_stratified.html'>Lrnr_stratified</a></code>,
<code><a href='Lrnr_subset_covariates.html'>Lrnr_subset_covariates</a></code>,
<code><a href='Lrnr_tsDyn.html'>Lrnr_tsDyn</a></code>,
<code><a href='Lrnr_ts_weights.html'>Lrnr_ts_weights</a></code>,
<code><a href='Lrnr_xgboost.html'>Lrnr_xgboost</a></code>,
<code><a href='Pipeline.html'>Pipeline</a></code>,
<code><a href='Stack.html'>Stack</a></code>,
<code><a href='define_h2o_X.html'>define_h2o_X</a>()</code>,
<code><a href='undocumented_learner.html'>undocumented_learner</a></code></p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>mtcars</span><span class='op'>)</span>
<span class='co'># create task for prediction</span>
<span class='va'>mtcars_task</span> <span class='op'><-</span> <span class='va'><a href='sl3_Task.html'>sl3_Task</a></span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span>
data <span class='op'>=</span> <span class='va'>mtcars</span>,
covariates <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>
<span class='st'>"cyl"</span>, <span class='st'>"disp"</span>, <span class='st'>"hp"</span>, <span class='st'>"drat"</span>, <span class='st'>"wt"</span>, <span class='st'>"qsec"</span>, <span class='st'>"vs"</span>, <span class='st'>"am"</span>,
<span class='st'>"gear"</span>, <span class='st'>"carb"</span>
<span class='op'>)</span>,
outcome <span class='op'>=</span> <span class='st'>"mpg"</span>
<span class='op'>)</span>
<span class='co'># initialization, training, and prediction with the defaults</span>
<span class='va'>svm_lrnr</span> <span class='op'><-</span> <span class='va'>Lrnr_svm</span><span class='op'>$</span><span class='fu'>new</span><span class='op'>(</span><span class='op'>)</span>
<span class='va'>svm_fit</span> <span class='op'><-</span> <span class='va'>svm_lrnr</span><span class='op'>$</span><span class='fu'>train</span><span class='op'>(</span><span class='va'>mtcars_task</span><span class='op'>)</span>
<span class='va'>svm_preds</span> <span class='op'><-</span> <span class='va'>svm_fit</span><span class='op'>$</span><span class='fu'>predict</span><span class='op'>(</span><span class='op'>)</span>
</div></pre>
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