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Linear Regression using p-value
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I'm trying to do Linear Regression, and I want to create a process which can exclude features using from Training Set by using p-value. So, if a column's p-value is less than 0.05 then remove/ignore that column and repeat the process until we are left with Statistically significant columns for out model.
Can someone guide me (or point me to any existing documentation) how to do it?
Can someone guide me (or point me to any existing documentation) how to do it?
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Answers
Did you try to set "T-test" in features selection parameter of linear regression operator? The alpha value 0.05 indicates to select variables with p<0.05.
Please let us know if you need any information.
Varun
https://www.varunmandalapu.com/
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Suppose you are building optimization process for feature selection that shave off the variables of less importance. You can use the “Forward Selection”, “Backward Elimination”, or other feature engineering operators. But we are using the performance (RMSE, AUC, accuracy, precision, recall, f-score, etc.) measurement of the predictive models not the p-value to select the significant variables.
In RapidMiner, only Generalized Linear Model or Logistic Regression could return a table of p-values. So we can build iterative loops to select important variables according to the p-values from GLM/LR. My attached process shows a simplified version (no iteration) of feature selection by p-values from GLM. Other non-linear models or ensembled regression models may not have p-values. Attached process is an example to drop variables with nonsignificant p-values. You will need the converters extension from Marketplace to run the process. The converter is used to extract the p-value from linear regression model to shave off the non-significant attributes...
Cheers,
YY