Regression models using multiple predictors

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The regression system in the previous blog post used only one predictor (gate oxide thickness) to predict a value of a response (threshold voltage or transconductance). A more complex system which uses four predictor inputs to determine a response is presented here.

The system uses the gate oxide thickness, gate length, gate voltage as standard predictors. Whereas, the saturation current is used as the 4th predictor to determine the threshold and vice versa. A Quadratic SVM Model in addition to the models used in previous system is used for training the data.

Below is a snapshot of the dataset used. It has 100 data-points for training and 10 data-points for test.

Threshold voltage prediction

Training the models for threshold voltage as the response, it is seen that the Squared Exponential Gaussian Process Regression Model has the least RMSE at 0.0097421.


The response plots (training data) for various regression models is shown below.





Testing the trained models, the following results are obtained.

Saturation current prediction

Training the models for saturation current as the response, it is seen that the Quadratic SVM Regression model has the least RMSE, with a value of 4.7553e-06.


The response plots (training data) for various regression models is shown below.





Testing the trained models, the following results are obtained.




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