How to optimize parameter in linear Regression?
hello!!
I create a multiple linear regression model by doing hyperparameter tuning using the operator optimize parameter, but I'm confused about what hyperparameters I should optimize in the rapid miner to avoid overfitting?
I hope someone can help me in solving this problem.
I create a multiple linear regression model by doing hyperparameter tuning using the operator optimize parameter, but I'm confused about what hyperparameters I should optimize in the rapid miner to avoid overfitting?
I hope someone can help me in solving this problem.
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thankyou @MarcoBarradas for reacting to my question. I have done it to do that way, but it still overfitting. did you know what hyperparameter can I use to optimize linear regression? and what is the ridge parameter in the linear regression operator?
this is the linear regression hyperparameter that I want to optimize, it is true? or maybe just 1 hyperparameter that i can optimize?
To avoid overfitting you should always split your data, we usually recommend to use Cross Validation that way you are sure that the model is able to generalize in the best way.
You can check the hows and why in this video
Cross Validation and model performance| RapidMiner Studio