Decision Tree in R : Metaprotein

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Decision Tree

We have implemented the following algorithms of Decision Trees for comparison of accuracy

• CART
• C 4.5
• C 5.0

Metaprotein Dataset

Metaproteins as rows and, patient data of three types with samples from each being tested for the presence of metaproteins in columns (along with metaprotein demographics)

To suit our decision tree model, we removed the demographic columns for the dataset and transposed the data frame to turn metaproteins into columns/variables & patients as rows.

We created a class label "Patient type" which has 3 factors - C, UC & CD

Metaprotein Dataset after pre-processing

We have taken half (1/2) of our Metaprotein Dataset to be used as Training Dataset & (1/2) to be used as Testing Dataset

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CART - Classification & Regression Trees

Confusion Matrix: Prediction on Test Dataset

Accuracy = 15/24 = 62.5 %

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Decision Tree (C 4.5)

Confusion Matrix: Prediction on Test Dataset

Accuracy = 17/24 = 70.8 %

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Decision Tree (C 5.0)

Confusion Matrix: Prediction on Test Dataset

Accuracy = 17/24 = 70.8 %

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