使用RapidMiner的线性回归预测波士顿房价

使用RapidMiner进行线性回归预测波士顿房价的步骤如下：

<process>
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.5.000" expanded="true" height="68" name="Retrieve Boston Housing" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Boston Housing"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="7.5.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">
        <parameter key="attribute_name" value="medv"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles">
          <parameter key="rm" value="ignore"/>
        </list>
      </operator>
      <operator activated="true" class="multiply" compatibility="7.5.000" expanded="true" height="82" name="Multiply" width="90" x="313" y="34">
        <parameter key="factor" value="1000"/>
        <list key="apply_to_attributes">
          <parameter key="medv" value="true"/>
        </list>
      </operator>
      <operator activated="true" class="linear_regression" compatibility="7.5.000" expanded="true" height="82" name="Linear Regression" width="90" x="447" y="34"/>
      <operator activated="true" class="apply_model" compatibility="7.5.000" expanded="true" height="82" name="Apply Model" width="90" x="581" y="34">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance" compatibility="7.5.000" expanded="true" height="82" name="Performance" width="90" x="715" y="34">
        <parameter key="main_criterion" value="mean_squared_error"/>
        <list key="other_criterion"/>
      </operator>
      <connect from_op="Retrieve Boston Housing" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Multiply" to_port="example set input"/>
      <connect from_op="Multiply" from_port="example set output" to_op="Linear Regression" to_port="training set"/>
      <connect from_op="Linear Regression" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="output" to_op="Performance" to_port="example set input"/>
      <connect from_op="Performance" from_port="performance" to_port="result 1"/>
      <connect from_op="Performance" from_port="model" to_port="result 2"/>
    </process>
  </operator>
</process>

步骤解释：
1. 使用retrieve操作符导入波士顿房价数据集。
2. 使用set_role操作符将medv属性设置为标签。
3. 使用multiply操作符将medv属性的值乘以1000，以便更好地进行回归分析。
4. 使用linear_regression操作符进行线性回归分析。
5. 使用apply_model操作符将模型应用于数据集。
6. 使用performance操作符评估模型的性能。