在Rapidminer中导出结果

Question

我在Rapid Miner中创建了一个神经网络模型，但是结果不是我所期望的，结果与某种中间产品有关，为了获得最终结果，我需要对由...生成的结果集进行自定义查询神经网络模型，现在的问题是：

1.How can I query the result set? 
2.Or how can I import that result set of neural net in a database then use read database operator to query it. 
3.Or how can I export the neural net model's result set in a csv file so I can Import it into a database for further processing?

Answer 1

训练神经网络时，首先要创建一个模型对象。然后，您需要做的就是将该模型应用于测试数据，该数据不应与用于训练的数据相同。 看看下面的示例过程（您也可以将xml复制并粘贴到RapidMiner进程窗口1中）：

要将结果导入数据库或csv文件中，有一种特殊的运算符，称为Write CSV或Write Database，稍后，您还必须首先在菜单项下定义连接< em>连接->管理数据库连接

您还可以查看RapidMiner社区的培训部分，那里有很多培训视频和相关材料：Free training material

1：

<?xml version="1.0" encoding="UTF-8"?><process version="8.2.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
<process expanded="true">
  <operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve Weighting" width="90" x="45" y="136">
    <parameter key="repository_entry" value="//Samples/data/Weighting"/>
  </operator>
  <operator activated="true" class="split_data" compatibility="8.2.000" expanded="true" height="103" name="Split Data" width="90" x="246" y="136">
    <enumeration key="partitions">
      <parameter key="ratio" value="0.7"/>
      <parameter key="ratio" value="0.3"/>
    </enumeration>
    <description align="center" color="yellow" colored="true" width="126">Split the data into training and a testing set (ratio 70% and 30%)</description>
  </operator>
  <operator activated="true" class="neural_net" compatibility="8.2.000" expanded="true" height="82" name="Neural Net" width="90" x="447" y="34">
    <list key="hidden_layers"/>
    <description align="center" color="green" colored="true" width="126">Train the neural net here</description>
  </operator>
  <operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model" width="90" x="648" y="136">
    <list key="application_parameters"/>
    <description align="center" color="green" colored="true" width="126">Apply the trained net on the test data</description>
  </operator>
  <operator activated="true" class="performance_classification" compatibility="8.2.000" expanded="true" height="82" name="Performance" width="90" x="841" y="136">
    <list key="class_weights"/>
    <description align="center" color="orange" colored="true" width="126">Check how well the network worked on the data and the see output of classification</description>
  </operator>
  <connect from_op="Retrieve Weighting" from_port="output" to_op="Split Data" to_port="example set"/>
  <connect from_op="Split Data" from_port="partition 1" to_op="Neural Net" to_port="training set"/>
  <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/>
  <connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/>
  <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="result 1"/>
  <connect from_op="Performance" from_port="example set" to_port="result 2"/>
  <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"/>
</process>
</operator>
</process>