Hello, everyone. This is my first forum post asking questions
about polynomial regression in rapidminer.
The original data is:x:4194.06 3466.45
2070.08 874.98 corresponding to y:91540.07
109460.36 120338.64 102182.19
As shown in the first flow, the first result expression is
obtained by using the polynomial regression operator.
<?xml version="1.0" encoding="UTF-8"?><process version="9.6.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.6.000" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="read_excel" compatibility="9.6.000" expanded="true" height="68" name="Read Excel" width="90" x="45" y="85">
<parameter key="excel_file" value="C:\Users\1\Desktop\question data.xlsx"/>
<parameter key="sheet_selection" value="sheet number"/>
<parameter key="sheet_number" value="1"/>
<parameter key="imported_cell_range" value="A1"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="first_row_as_names" value="true"/>
<list key="annotations"/>
<parameter key="date_format" value=""/>
<parameter key="time_zone" value="SYSTEM"/>
<parameter key="locale" value="English (United States)"/>
<parameter key="read_all_values_as_polynominal" value="false"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="x.true.real.attribute"/>
<parameter key="1" value="y.true.real.attribute"/>
</list>
<parameter key="read_not_matching_values_as_missings" value="false"/>
<parameter key="datamanagement" value="double_array"/>
<parameter key="data_management" value="auto"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="85">
<parameter key="attribute_name" value="y"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles">
<parameter key="x" value="regular"/>
</list>
</operator>
<operator activated="true" class="polynomial_regression" compatibility="9.6.000" expanded="true" height="82" name="Polynomial Regression" width="90" x="313" y="85">
<parameter key="max_iterations" value="5000"/>
<parameter key="replication_factor" value="2"/>
<parameter key="max_degree" value="2"/>
<parameter key="min_coefficient" value="-100.0"/>
<parameter key="max_coefficient" value="100.0"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
</operator>
<connect from_op="Read Excel" from_port="output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Polynomial Regression" to_port="training set"/>
<connect from_op="Polynomial Regression" from_port="model" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
The second flow, based on the original data, creates a new list of attributes as x^2=z, and uses the linear regression operator to make the second result expression.
<?xml version="1.0" encoding="UTF-8"?><process version="9.6.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.6.000" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="read_excel" compatibility="9.6.000" expanded="true" height="68" name="Read Excel" width="90" x="45" y="85">
<parameter key="excel_file" value="C:\Users\1\Desktop\question data.xlsx"/>
<parameter key="sheet_selection" value="sheet number"/>
<parameter key="sheet_number" value="1"/>
<parameter key="imported_cell_range" value="A1"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="first_row_as_names" value="true"/>
<list key="annotations"/>
<parameter key="date_format" value=""/>
<parameter key="time_zone" value="SYSTEM"/>
<parameter key="locale" value="English (United States)"/>
<parameter key="read_all_values_as_polynominal" value="false"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="x.true.real.attribute"/>
<parameter key="1" value="y.true.real.attribute"/>
</list>
<parameter key="read_not_matching_values_as_missings" value="false"/>
<parameter key="datamanagement" value="double_array"/>
<parameter key="data_management" value="auto"/>
</operator>
<operator activated="true" class="generate_attributes" compatibility="9.6.000" expanded="true" height="82" name="Generate Attributes" width="90" x="179" y="85">
<list key="function_descriptions">
<parameter key="z" value="x*x"/>
</list>
<parameter key="keep_all" value="true"/>
</operator>
<operator activated="false" class="rename" compatibility="9.6.000" expanded="true" height="82" name="Rename" width="90" x="246" y="238">
<parameter key="old_name" value="x"/>
<parameter key="new_name" value="x^2"/>
<list key="rename_additional_attributes"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="313" y="85">
<parameter key="attribute_name" value="y"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles">
<parameter key="x" value="regular"/>
</list>
</operator>
<operator activated="true" class="linear_regression" compatibility="9.6.000" expanded="true" height="103" name="Linear Regression" width="90" x="514" y="85">
<parameter key="feature_selection" value="none"/>
<parameter key="alpha" value="0.05"/>
<parameter key="max_iterations" value="10"/>
<parameter key="forward_alpha" value="0.05"/>
<parameter key="backward_alpha" value="0.05"/>
<parameter key="eliminate_colinear_features" value="false"/>
<parameter key="min_tolerance" value="0.05"/>
<parameter key="use_bias" value="true"/>
<parameter key="ridge" value="1.0E-8"/>
</operator>
<operator activated="false" class="polynomial_regression" compatibility="9.6.000" expanded="true" height="82" name="Polynomial Regression" width="90" x="581" y="238">
<parameter key="max_iterations" value="5000"/>
<parameter key="replication_factor" value="2"/>
<parameter key="max_degree" value="2"/>
<parameter key="min_coefficient" value="-100.0"/>
<parameter key="max_coefficient" value="100.0"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
</operator>
<connect from_op="Read Excel" from_port="output" to_op="Generate Attributes" to_port="example set input"/>
<connect from_op="Generate Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Linear Regression" to_port="training set"/>
<connect from_op="Linear Regression" from_port="model" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
I want to ask why the results of the two processes are not
the same, the original data presents a quadratic nonlinear relationship, and
why the quadratic expression cannot be made by polynomial regression.