我正在 Rapidminer 中开发一个简单的神经网络模型来预测每小时通过高速公路的汽车数量。在一天的早些时候（凌晨 2:00 到早上 6:00），高速公路上几乎没有汽车，因此，有时我的模型会预测负值。
如何将输出变量限制为正值？
这始终取决于数据和您想要做什么，但一种方法是将数字转换为多项式。所以 0 变成字符串“0”，1 变成“1”，依此类推。这迫使神经网络单独使用可用值。
这是使用虚拟数据的示例流程。
<?xml version="1.0" encoding="UTF-8"?><process version="7.3.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="7.3.001" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="subprocess" compatibility="7.3.001" expanded="true" height="82" name="Subprocess" width="90" x="246" y="34">
<process expanded="true">
<operator activated="true" class="generate_data" compatibility="7.3.001" expanded="true" height="68" name="Generate Data" width="90" x="45" y="34">
<parameter key="target_function" value="polynomial"/>
<parameter key="attributes_lower_bound" value="0.0"/>
<parameter key="attributes_upper_bound" value="3.0"/>
</operator>
<operator activated="true" class="normalize" compatibility="7.3.001" expanded="true" height="103" name="Normalize" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="label"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="method" value="range transformation"/>
<parameter key="max" value="4.99"/>
</operator>
<operator activated="true" class="real_to_integer" compatibility="7.3.001" expanded="true" height="82" name="Real to Integer" width="90" x="313" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="label"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<connect from_op="Generate Data" from_port="output" to_op="Normalize" to_port="example set input"/>
<connect from_op="Normalize" from_port="example set output" to_op="Real to Integer" to_port="example set input"/>
<connect from_op="Real to Integer" from_port="example set output" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="numerical_to_polynominal" compatibility="7.3.001" expanded="true" height="82" name="Numerical to Polynominal" width="90" x="380" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="label"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="7.3.001" expanded="true" height="145" name="Validation" width="90" x="514" y="34">
<parameter key="sampling_type" value="shuffled sampling"/>
<process expanded="true">
<operator activated="true" class="neural_net" compatibility="7.3.001" expanded="true" height="82" name="Neural Net" width="90" x="323" y="34">
<list key="hidden_layers"/>
</operator>
<connect from_port="training set" to_op="Neural Net" to_port="training set"/>
<connect from_op="Neural Net" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="7.3.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="7.3.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="performance 1"/>
<connect from_op="Performance" from_port="example set" to_port="test set results"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="nominal_to_numerical" compatibility="7.3.001" expanded="true" height="103" name="Nominal to Numerical (2)" width="90" x="715" y="136">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value="label"/>
<parameter key="attributes" value="prediction(label)|label"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="coding_type" value="unique integers"/>
<list key="comparison_groups"/>
</operator>
<connect from_op="Subprocess" from_port="out 1" to_op="Numerical to Polynominal" to_port="example set input"/>
<connect from_op="Numerical to Polynominal" from_port="example set output" to_op="Validation" to_port="example set"/>
<connect from_op="Validation" from_port="model" to_port="result 1"/>
<connect from_op="Validation" from_port="example set" to_port="result 2"/>
<connect from_op="Validation" from_port="test result set" to_op="Nominal to Numerical (2)" to_port="example set input"/>
<connect from_op="Validation" from_port="performance 1" to_port="result 4"/>
<connect from_op="Nominal to Numerical (2)" from_port="example set output" to_port="result 3"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 4" spacing="0"/>
<portSpacing port="sink_result 5" spacing="0"/>
</process>
</operator>
</process>
它制作虚拟数据并将数值转换为多项式。
Cross Validation不用说，这可能不适合您想要的，但这是一个开始。
安德鲁
您已经重新调整了神经网络参数，否则您无法访问RapidMiner中算法的详细信息。其他想法是在神经网络模型之后使用阈值运算符，以便您可以更改决策的边界，以便它预测的负面结果会比现在少。