📄 learner.html
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<TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor"><TH ALIGN="left" COLSPAN="2">Classes in <A HREF="../../../../../com/rapidminer/operator/learner/lazy/package-summary.html">com.rapidminer.operator.learner.lazy</A> that implement <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/lazy/AttributeBasedVotingLearner.html" title="class in com.rapidminer.operator.learner.lazy">AttributeBasedVotingLearner</A></B></CODE><BR> AttributeBasedVotingLearner is very lazy.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/lazy/DefaultLearner.html" title="class in com.rapidminer.operator.learner.lazy">DefaultLearner</A></B></CODE><BR> This learner creates a model, that will simply predict a default value for all examples, i.e. the average or median of the true labels (or the mode in case of classification) or a fixed specified value.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/lazy/KNNLearner.html" title="class in com.rapidminer.operator.learner.lazy">KNNLearner</A></B></CODE><BR> A k nearest neighbor implementation.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.meta"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2">Uses of <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/meta/package-summary.html">com.rapidminer.operator.learner.meta</A></FONT></TH></TR></TABLE> <P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor"><TH ALIGN="left" COLSPAN="2">Classes in <A HREF="../../../../../com/rapidminer/operator/learner/meta/package-summary.html">com.rapidminer.operator.learner.meta</A> that implement <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AbstractMetaLearner.html" title="class in com.rapidminer.operator.learner.meta">AbstractMetaLearner</A></B></CODE><BR> A <tt>MetaLearner</tt> is an operator that encapsulates one or more learning steps to build its model.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AbstractStacking.html" title="class in com.rapidminer.operator.learner.meta">AbstractStacking</A></B></CODE><BR> This class uses n+1 inner learners and generates n different models by using the last n learners.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdaBoost.html" title="class in com.rapidminer.operator.learner.meta">AdaBoost</A></B></CODE><BR> This AdaBoost implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/AdditiveRegression.html" title="class in com.rapidminer.operator.learner.meta">AdditiveRegression</A></B></CODE><BR> This operator uses regression learner as a base learner.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Bagging.html" title="class in com.rapidminer.operator.learner.meta">Bagging</A></B></CODE><BR> This Bagging implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayBoostStream.html" title="class in com.rapidminer.operator.learner.meta">BayBoostStream</A></B></CODE><BR> Assumptions: target label is always boolean goal is to fit a crisp ensemble classifier (use_distribution always off) base classifier weights are always adapted by a single row from first to last no internal bootstrapping </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/BayesianBoosting.html" title="class in com.rapidminer.operator.learner.meta">BayesianBoosting</A></B></CODE><BR> This operator trains an ensemble of classifiers for boolean target attributes.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Binary2MultiClassLearner.html" title="class in com.rapidminer.operator.learner.meta">Binary2MultiClassLearner</A></B></CODE><BR> A metaclassifier for handling multi-class datasets with 2-class classifiers.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/ClassificationByRegression.html" title="class in com.rapidminer.operator.learner.meta">ClassificationByRegression</A></B></CODE><BR> For a classified dataset (with possibly more than two classes) builds a classifier using a regression method which is specified by the inner operator.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/CostBasedThresholdLearner.html" title="class in com.rapidminer.operator.learner.meta">CostBasedThresholdLearner</A></B></CODE><BR> This operator uses a set of class weights and also allows a weight for the fact that an example is not classified at all (marked as unknown).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/MetaCost.html" title="class in com.rapidminer.operator.learner.meta">MetaCost</A></B></CODE><BR> This operator uses a given cost matrix to compute label predictions according to classification costs.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/RelativeRegression.html" title="class in com.rapidminer.operator.learner.meta">RelativeRegression</A></B></CODE><BR> This meta regression learner transforms the label on-the-fly relative to the value of the specified attribute.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Stacking.html" title="class in com.rapidminer.operator.learner.meta">Stacking</A></B></CODE><BR> This class uses n+1 inner learners and generates n different models by using the last n learners.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Tree2RuleConverter.html" title="class in com.rapidminer.operator.learner.meta">Tree2RuleConverter</A></B></CODE><BR> This meta learner uses an inner tree learner and creates a rule model from the learned decision tree.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/meta/Vote.html" title="class in com.rapidminer.operator.learner.meta">Vote</A></B></CODE><BR> This class uses n+1 inner learners and generates n different models by using the last n learners.</TD></TR></TABLE> <P><A NAME="com.rapidminer.operator.learner.rules"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2">Uses of <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A> in <A HREF="../../../../../com/rapidminer/operator/learner/rules/package-summary.html">com.rapidminer.operator.learner.rules</A></FONT></TH></TR></TABLE> <P><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableSubHeadingColor"><TH ALIGN="left" COLSPAN="2">Classes in <A HREF="../../../../../com/rapidminer/operator/learner/rules/package-summary.html">com.rapidminer.operator.learner.rules</A> that implement <A HREF="../../../../../com/rapidminer/operator/learner/Learner.html" title="interface in com.rapidminer.operator.learner">Learner</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/rules/BestRuleInduction.html" title="class in com.rapidminer.operator.learner.rules">BestRuleInduction</A></B></CODE><BR> This operator returns the best rule regarding WRAcc using exhaustive search.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../../com/rapidminer/operator/learner/rules/RuleLearner.html" title="class in com.rapidminer.operator.learner.rules">RuleLearner</A></B></CODE><BR> This operator works similar to the propositional rule learner named Repeated Incremental Pruning to Produce Error Reduction (RIPPER, Cohen 1995).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> class</CODE></FONT></TD>⌨️ 快捷键说明
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