我正在Rapidminer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。很明显，在清晨（从早上2点到早上6点），很少有车在高速公路上，有时候我的模型会预测汽车的数量为负数（如-2或-3），这是从统计学上可以理解，但是当你想在某个地方报告时，这并不酷。
我正在寻找一种方法来对模型施加约束，以便它只能预测正数。我怎么能这样做？
由于
答案 0 :(得分：1)
它总是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。因此0变为字符串“0”，1变为“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过程。
<?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>
它生成虚拟数据并将数值转换为多项式。 Cross Validation的预测示例集输出包含多项式，并将这些输出转换回数字。
毋庸置疑，这可能不适合你想要的东西，但这是一个开始。
安德鲁
答案 1 :(得分：0)
您已重新调整神经网络参数，否则您无法访问RapidMiner中的算法细节。其他想法是在神经网络模型之后使用阈值算子，这样你就可以改变决策的边界，这样它就可以预测负数少于现在。