Classification Models to determine operating region of a MOSFET

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The datasets are imported from an excel spreadsheet to MATLAB in the form of tables. The training data is used to train various classification models. 

The training data has 100 data-points, whereas the test data has 15 data data-points.

The training is done to determine the region of operation of a MOSFET for a given values of threshold voltage, gate-to-source and drain-to-source voltage. In all the classification models, the cross-validation parameter is set to 5. The training data is divided in to 5 groups to verify the accuracy in each group while also preventing over-fitting the data. The accuracy of a model maybe determined by a confusion matrix. It is a performance measurement for machine learning classification models where there are two or more classes. It is a table with 4 different combinations of predicted and actual classes. It gives the count of prediction of true positives and negatives (correct classifications), false positives and negatives (erroneous classifications).

The training system in MATLAB works such that the accuracy of a trained model is determined using the training data itself. The training of various models using the training dataset produces the following confusion matrices. It is seen that the Logistic Regression model has the highest accuracy at 97%, with 3 wrong classifications.


Below are the confusions matrices for various classification training models (training data).




On using the test data on the trained models, it is seen that the Logistic Regression Classification predicts the operating regions for all test cases correctly, while the other models have varying accuracy.

Confusion Matrix for Logistic Regression classification model (test dataset)


Confusion Matrix for Quadratic Discriminant classification model (test dataset)


Confusion Matrix for Weighted KNN classification model (test dataset)


Confusion Matrix for Fine Gaussian SVM classification model (test dataset)

The accuracy of each model maybe summed up as follows.


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