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<h1 id="index">Index</h1>

<div class="genindex-jumpbox">
 <a href="#A"><strong>A</strong></a>
 | <a href="#B"><strong>B</strong></a>
 | <a href="#C"><strong>C</strong></a>
 | <a href="#D"><strong>D</strong></a>
 | <a href="#E"><strong>E</strong></a>
 | <a href="#F"><strong>F</strong></a>
 | <a href="#G"><strong>G</strong></a>
 | <a href="#H"><strong>H</strong></a>
 | <a href="#I"><strong>I</strong></a>
 | <a href="#K"><strong>K</strong></a>
 | <a href="#L"><strong>L</strong></a>
 | <a href="#M"><strong>M</strong></a>
 | <a href="#N"><strong>N</strong></a>
 | <a href="#P"><strong>P</strong></a>
 | <a href="#R"><strong>R</strong></a>
 | <a href="#S"><strong>S</strong></a>
 | <a href="#T"><strong>T</strong></a>
 | <a href="#U"><strong>U</strong></a>
 
</div>
<h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.AbstractLossFunction">AbstractLossFunction (class in hep_ml.losses)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkClassifier">AbstractNeuralNetworkClassifier (class in hep_ml.nnet)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkRegressor">AbstractNeuralNetworkRegressor (class in hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.adadelta_trainer">adadelta_trainer() (in module hep_ml.nnet)</a>
</li>
      <li><a href="losses.html#hep_ml.losses.AdaLossFunction">AdaLossFunction (class in hep_ml.losses)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="B">B</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="metrics.html#hep_ml.metrics.BinBasedCvM">BinBasedCvM (class in hep_ml.metrics)</a>
</li>
      <li><a href="metrics.html#hep_ml.metrics.BinBasedSDE">BinBasedSDE (class in hep_ml.metrics)</a>
</li>
      <li><a href="metrics.html#hep_ml.metrics.BinBasedTheil">BinBasedTheil (class in hep_ml.metrics)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.BinFlatnessLossFunction">BinFlatnessLossFunction (class in hep_ml.losses)</a>
</li>
      <li><a href="reweight.html#hep_ml.reweight.BinsReweighter">BinsReweighter (class in hep_ml.reweight)</a>
</li>
      <li><a href="preprocessing.html#hep_ml.preprocessing.BinTransformer">BinTransformer (class in hep_ml.preprocessing)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="C">C</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.check_dimensions">check_dimensions() (hep_ml.speedup.LookupClassifier method)</a>
</li>
      <li><a href="losses.html#hep_ml.losses.CompositeLossFunction">CompositeLossFunction (class in hep_ml.losses)</a>
</li>
      <li><a href="reweight.html#hep_ml.reweight.BinsReweighter.compute_bin_indices">compute_bin_indices() (hep_ml.reweight.BinsReweighter method)</a>
</li>
      <li><a href="losses.html#hep_ml.losses.AbstractLossFunction.compute_optimal_step">compute_optimal_step() (hep_ml.losses.AbstractLossFunction method)</a>

      <ul>
        <li><a href="losses.html#hep_ml.losses.MAELossFunction.compute_optimal_step">(hep_ml.losses.MAELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MSELossFunction.compute_optimal_step">(hep_ml.losses.MSELossFunction method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.KnnAdaLossFunction.compute_parameters">compute_parameters() (hep_ml.losses.KnnAdaLossFunction method)</a>
</li>
      <li><a href="splot.html#hep_ml.splot.compute_sweights">compute_sweights() (in module hep_ml.splot)</a>
</li>
      <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.convert_bins_to_lookup_index">convert_bins_to_lookup_index() (hep_ml.speedup.LookupClassifier method)</a>
</li>
      <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.convert_lookup_index_to_bins">convert_lookup_index_to_bins() (hep_ml.speedup.LookupClassifier method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="D">D</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.decision_function">decision_function() (hep_ml.uboost.uBoostBDT method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="E">E</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.exp_log_loss">exp_log_loss() (in module hep_ml.nnet)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.exp_loss">exp_loss() (in module hep_ml.nnet)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="F">F</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.feature_importances_">feature_importances_ (hep_ml.uboost.uBoostBDT property)</a>
</li>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingClassifier.fit">fit() (hep_ml.gradientboosting.UGradientBoostingClassifier method)</a>

      <ul>
        <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingRegressor.fit">(hep_ml.gradientboosting.UGradientBoostingRegressor method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.AbstractLossFunction.fit">(hep_ml.losses.AbstractLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.AdaLossFunction.fit">(hep_ml.losses.AdaLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.CompositeLossFunction.fit">(hep_ml.losses.CompositeLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.LogLossFunction.fit">(hep_ml.losses.LogLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MAELossFunction.fit">(hep_ml.losses.MAELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MSELossFunction.fit">(hep_ml.losses.MSELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.RankBoostLossFunction.fit">(hep_ml.losses.RankBoostLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.ReweightLossFunction.fit">(hep_ml.losses.ReweightLossFunction method)</a>
</li>
        <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkClassifier.fit">(hep_ml.nnet.AbstractNeuralNetworkClassifier method)</a>
</li>
        <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkRegressor.fit">(hep_ml.nnet.AbstractNeuralNetworkRegressor method)</a>
</li>
        <li><a href="preprocessing.html#hep_ml.preprocessing.BinTransformer.fit">(hep_ml.preprocessing.BinTransformer method)</a>
</li>
        <li><a href="preprocessing.html#hep_ml.preprocessing.IronTransformer.fit">(hep_ml.preprocessing.IronTransformer method)</a>
</li>
        <li><a href="reweight.html#hep_ml.reweight.BinsReweighter.fit">(hep_ml.reweight.BinsReweighter method)</a>
</li>
        <li><a href="reweight.html#hep_ml.reweight.FoldingReweighter.fit">(hep_ml.reweight.FoldingReweighter method)</a>
</li>
        <li><a href="reweight.html#hep_ml.reweight.GBReweighter.fit">(hep_ml.reweight.GBReweighter method)</a>
</li>
        <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.fit">(hep_ml.speedup.LookupClassifier method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.fit">(hep_ml.uboost.uBoostBDT method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostClassifier.fit">(hep_ml.uboost.uBoostClassifier method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="reweight.html#hep_ml.reweight.FoldingReweighter">FoldingReweighter (class in hep_ml.reweight)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="reweight.html#hep_ml.reweight.GBReweighter">GBReweighter (class in hep_ml.reweight)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="H">H</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li>
    hep_ml.gradientboosting

      <ul>
        <li><a href="gb.html#module-hep_ml.gradientboosting">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.losses

      <ul>
        <li><a href="losses.html#module-hep_ml.losses">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.metrics

      <ul>
        <li><a href="metrics.html#module-hep_ml.metrics">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.nnet

      <ul>
        <li><a href="nnet.html#module-hep_ml.nnet">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.preprocessing

      <ul>
        <li><a href="preprocessing.html#module-hep_ml.preprocessing">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.reweight

      <ul>
        <li><a href="reweight.html#module-hep_ml.reweight">module</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li>
    hep_ml.speedup

      <ul>
        <li><a href="speedup.html#module-hep_ml.speedup">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.splot

      <ul>
        <li><a href="splot.html#module-hep_ml.splot">module</a>
</li>
      </ul></li>
      <li>
    hep_ml.uboost

      <ul>
        <li><a href="uboost.html#module-hep_ml.uboost">module</a>
</li>
      </ul></li>
      <li><a href="losses.html#hep_ml.losses.AdaLossFunction.hessian">hessian() (hep_ml.losses.AdaLossFunction method)</a>

      <ul>
        <li><a href="losses.html#hep_ml.losses.CompositeLossFunction.hessian">(hep_ml.losses.CompositeLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.LogLossFunction.hessian">(hep_ml.losses.LogLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MSELossFunction.hessian">(hep_ml.losses.MSELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.RankBoostLossFunction.hessian">(hep_ml.losses.RankBoostLossFunction method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="I">I</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="preprocessing.html#hep_ml.preprocessing.IronTransformer">IronTransformer (class in hep_ml.preprocessing)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.irprop_minus_trainer">irprop_minus_trainer() (in module hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.irprop_plus_trainer">irprop_plus_trainer() (in module hep_ml.nnet)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="K">K</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.KnnAdaLossFunction">KnnAdaLossFunction (class in hep_ml.losses)</a>
</li>
      <li><a href="metrics.html#hep_ml.metrics.KnnBasedCvM">KnnBasedCvM (class in hep_ml.metrics)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="metrics.html#hep_ml.metrics.KnnBasedSDE">KnnBasedSDE (class in hep_ml.metrics)</a>
</li>
      <li><a href="metrics.html#hep_ml.metrics.KnnBasedTheil">KnnBasedTheil (class in hep_ml.metrics)</a>
</li>
      <li><a href="losses.html#hep_ml.losses.KnnFlatnessLossFunction">KnnFlatnessLossFunction (class in hep_ml.losses)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="L">L</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.log_loss">log_loss() (in module hep_ml.nnet)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.LogLossFunction">LogLossFunction (class in hep_ml.losses)</a>
</li>
      <li><a href="speedup.html#hep_ml.speedup.LookupClassifier">LookupClassifier (class in hep_ml.speedup)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="M">M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.MAELossFunction">MAELossFunction (class in hep_ml.losses)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.MLPClassifier">MLPClassifier (class in hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.MLPMultiClassifier">MLPMultiClassifier (class in hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.MLPRegressor">MLPRegressor (class in hep_ml.nnet)</a>
</li>
      <li>
    module

      <ul>
        <li><a href="gb.html#module-hep_ml.gradientboosting">hep_ml.gradientboosting</a>
</li>
        <li><a href="losses.html#module-hep_ml.losses">hep_ml.losses</a>
</li>
        <li><a href="metrics.html#module-hep_ml.metrics">hep_ml.metrics</a>
</li>
        <li><a href="nnet.html#module-hep_ml.nnet">hep_ml.nnet</a>
</li>
        <li><a href="preprocessing.html#module-hep_ml.preprocessing">hep_ml.preprocessing</a>
</li>
        <li><a href="reweight.html#module-hep_ml.reweight">hep_ml.reweight</a>
</li>
        <li><a href="speedup.html#module-hep_ml.speedup">hep_ml.speedup</a>
</li>
        <li><a href="splot.html#module-hep_ml.splot">hep_ml.splot</a>
</li>
        <li><a href="uboost.html#module-hep_ml.uboost">hep_ml.uboost</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.mse_loss">mse_loss() (in module hep_ml.nnet)</a>
</li>
      <li><a href="losses.html#hep_ml.losses.MSELossFunction">MSELossFunction (class in hep_ml.losses)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="N">N</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.AdaLossFunction.negative_gradient">negative_gradient() (hep_ml.losses.AdaLossFunction method)</a>

      <ul>
        <li><a href="losses.html#hep_ml.losses.CompositeLossFunction.negative_gradient">(hep_ml.losses.CompositeLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.LogLossFunction.negative_gradient">(hep_ml.losses.LogLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MAELossFunction.negative_gradient">(hep_ml.losses.MAELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MSELossFunction.negative_gradient">(hep_ml.losses.MSELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.RankBoostLossFunction.negative_gradient">(hep_ml.losses.RankBoostLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.ReweightLossFunction.negative_gradient">(hep_ml.losses.ReweightLossFunction method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="P">P</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.PairwiseNeuralNetwork">PairwiseNeuralNetwork (class in hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.PairwiseSoftplusNeuralNetwork">PairwiseSoftplusNeuralNetwork (class in hep_ml.nnet)</a>
</li>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingClassifier.predict">predict() (hep_ml.gradientboosting.UGradientBoostingClassifier method)</a>

      <ul>
        <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingRegressor.predict">(hep_ml.gradientboosting.UGradientBoostingRegressor method)</a>
</li>
        <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkClassifier.predict">(hep_ml.nnet.AbstractNeuralNetworkClassifier method)</a>
</li>
        <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkRegressor.predict">(hep_ml.nnet.AbstractNeuralNetworkRegressor method)</a>
</li>
        <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.predict">(hep_ml.speedup.LookupClassifier method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.predict">(hep_ml.uboost.uBoostBDT method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostClassifier.predict">(hep_ml.uboost.uBoostClassifier method)</a>
</li>
      </ul></li>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingClassifier.predict_proba">predict_proba() (hep_ml.gradientboosting.UGradientBoostingClassifier method)</a>

      <ul>
        <li><a href="nnet.html#hep_ml.nnet.AbstractNeuralNetworkClassifier.predict_proba">(hep_ml.nnet.AbstractNeuralNetworkClassifier method)</a>
</li>
        <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.predict_proba">(hep_ml.speedup.LookupClassifier method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.predict_proba">(hep_ml.uboost.uBoostBDT method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostClassifier.predict_proba">(hep_ml.uboost.uBoostClassifier method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="reweight.html#hep_ml.reweight.BinsReweighter.predict_weights">predict_weights() (hep_ml.reweight.BinsReweighter method)</a>

      <ul>
        <li><a href="reweight.html#hep_ml.reweight.FoldingReweighter.predict_weights">(hep_ml.reweight.FoldingReweighter method)</a>
</li>
        <li><a href="reweight.html#hep_ml.reweight.GBReweighter.predict_weights">(hep_ml.reweight.GBReweighter method)</a>
</li>
      </ul></li>
      <li><a href="losses.html#hep_ml.losses.AbstractLossFunction.prepare_new_leaves_values">prepare_new_leaves_values() (hep_ml.losses.AbstractLossFunction method)</a>

      <ul>
        <li><a href="losses.html#hep_ml.losses.MAELossFunction.prepare_new_leaves_values">(hep_ml.losses.MAELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.RankBoostLossFunction.prepare_new_leaves_values">(hep_ml.losses.RankBoostLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.ReweightLossFunction.prepare_new_leaves_values">(hep_ml.losses.ReweightLossFunction method)</a>
</li>
      </ul></li>
      <li><a href="losses.html#hep_ml.losses.AbstractLossFunction.prepare_tree_params">prepare_tree_params() (hep_ml.losses.AbstractLossFunction method)</a>

      <ul>
        <li><a href="losses.html#hep_ml.losses.AdaLossFunction.prepare_tree_params">(hep_ml.losses.AdaLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.LogLossFunction.prepare_tree_params">(hep_ml.losses.LogLossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MAELossFunction.prepare_tree_params">(hep_ml.losses.MAELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.MSELossFunction.prepare_tree_params">(hep_ml.losses.MSELossFunction method)</a>
</li>
        <li><a href="losses.html#hep_ml.losses.ReweightLossFunction.prepare_tree_params">(hep_ml.losses.ReweightLossFunction method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="R">R</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="losses.html#hep_ml.losses.RankBoostLossFunction">RankBoostLossFunction (class in hep_ml.losses)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.RBFNeuralNetwork">RBFNeuralNetwork (class in hep_ml.nnet)</a>
</li>
      <li><a href="losses.html#hep_ml.losses.ReweightLossFunction">ReweightLossFunction (class in hep_ml.losses)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="nnet.html#hep_ml.nnet.sgd_trainer">sgd_trainer() (in module hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.SimpleNeuralNetwork">SimpleNeuralNetwork (class in hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.smooth_huber_loss">smooth_huber_loss() (in module hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.SoftmaxNeuralNetwork">SoftmaxNeuralNetwork (class in hep_ml.nnet)</a>
</li>
      <li><a href="nnet.html#hep_ml.nnet.squared_loss">squared_loss() (in module hep_ml.nnet)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.staged_decision_function">staged_decision_function() (hep_ml.uboost.uBoostBDT method)</a>
</li>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingRegressor.staged_predict">staged_predict() (hep_ml.gradientboosting.UGradientBoostingRegressor method)</a>
</li>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingClassifier.staged_predict_proba">staged_predict_proba() (hep_ml.gradientboosting.UGradientBoostingClassifier method)</a>

      <ul>
        <li><a href="uboost.html#hep_ml.uboost.uBoostBDT.staged_predict_proba">(hep_ml.uboost.uBoostBDT method)</a>
</li>
        <li><a href="uboost.html#hep_ml.uboost.uBoostClassifier.staged_predict_proba">(hep_ml.uboost.uBoostClassifier method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="T">T</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="preprocessing.html#hep_ml.preprocessing.BinTransformer.transform">transform() (hep_ml.preprocessing.BinTransformer method)</a>

      <ul>
        <li><a href="preprocessing.html#hep_ml.preprocessing.IronTransformer.transform">(hep_ml.preprocessing.IronTransformer method)</a>
</li>
        <li><a href="speedup.html#hep_ml.speedup.LookupClassifier.transform">(hep_ml.speedup.LookupClassifier method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="U">U</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="uboost.html#hep_ml.uboost.uBoostBDT">uBoostBDT (class in hep_ml.uboost)</a>
</li>
      <li><a href="uboost.html#hep_ml.uboost.uBoostClassifier">uBoostClassifier (class in hep_ml.uboost)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingClassifier">UGradientBoostingClassifier (class in hep_ml.gradientboosting)</a>
</li>
      <li><a href="gb.html#hep_ml.gradientboosting.UGradientBoostingRegressor">UGradientBoostingRegressor (class in hep_ml.gradientboosting)</a>
</li>
  </ul></td>
</tr></table>



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