linear regression optimization
Greeting,
I need to predict thermal expand range from tempreture, and I get a test dataset, so I try the linear regression but the result is not good, my setting and data is like below, can you help me to improve the prediction ? thanks.
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From looking at the scatter plot of the two variables you get a sense that there are other important predictors missing from this equation. There is non-linearity so you could use other methods instead of plain vanilla linear regression.
Investigate further the physics of the process. I know absolutely nothing and Wikipidea tells me pressure is another important variable.
my friend do the same job with matlab and the result is well fit the test data , but I can not take the same score with rapidminer. I am still confused about this ...
Can you post your RapidMiner process? Maybe we can help troubleshoot.
hi,
you can see the rm process and operator setting in the attachment above, and the origin data is also there.
I can get a simular output like matlab, when I change the input attribute from "tempreture" to "tempreture change", which means y=kx+b do not work but y=k(x-x1)+b works well in rm, while y=kx+b works well in matlab.
I am confused about this.
Altair Employee@dkpengqiuyang did you try to delete collinear feature?
Process is attached here.
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.000">
<operator activated="true" class="retrieve" compatibility="7.6.000" expanded="true" height="68" name="Retrieve regression" width="90" x="45" y="34">
<parameter key="repository_entry" value="//RM YY Local Repository/AAA-PROSPECT/data/regression"/>
</operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.000">
<operator activated="true" class="set_role" compatibility="7.6.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">
<parameter key="attribute_name" value="thermal expand"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.000">
<operator activated="true" class="linear_regression" compatibility="7.6.000" expanded="true" height="103" name="Linear Regression" width="90" x="380" y="34">
<parameter key="feature_selection" value="M5 prime"/>
<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>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.000">
<operator activated="true" class="apply_model" compatibility="7.6.000" expanded="true" height="82" name="Apply Model" width="90" x="514" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
</process>
From looking at the scatter plot of the two variables you get a sense that there are other important predictors missing from this equation. There is non-linearity so you could use other methods instead of plain vanilla linear regression.
Investigate further the physics of the process. I know absolutely nothing and Wikipidea tells me pressure is another important variable.
you can use optimization parameter (grid)operator to get the best parameter for your dataset