The SVM has achieved 0.83 of accuracy. Accuracy refers to the percentage of correct predictions made by our model when compared with the actual classifications in the test data. This model has accurately classified 83% of the patients based on the attributes available in our dataset such as age, sex, smoking etc. Moreover, the decision tree algorithm have achieved accuracy up to 0.79.
A confusion matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data. The matrix is n-by-n, where n is the number of classes. Table 1 shows a confusion matrix for a binary classification model. The rows present the number of actual classifications in the test data. The columns present the number of predicted classifications made by the model.
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